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1.
Int J Med Sci ; 17(16): 2511-2530, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33029094

RESUMO

ShuFeng JieDu capsule (SFJDC), a traditional Chinese medicine, has been recommended for the treatment of COVID-19 infections. However, the pharmacological mechanism of SFJDC still remains vague to date. The active ingredients and their target genes of SFJDC were collected from TCMSP. COVID-19 is a type of Novel Coronavirus Pneumonia (NCP). NCP-related target genes were collected from GeneCards database. The ingredients-targets network of SFJDC and PPI networks were constructed. The candidate genes were screened by Venn diagram package for enrichment analysis. The gene-pathway network was structured to obtain key target genes. In total, 124 active ingredients, 120 target genes of SFJDC and 251 NCP-related target genes were collected. The functional annotations cluster 1 of 23 candidate genes (CGs) were related to lung and Virus infection. RELA, MAPK1, MAPK14, CASP3, CASP8 and IL6 were the key target genes. The results suggested that SFJDC cloud be treated COVID-19 by multi-compounds and multi-pathways, and this study showed that the mechanism of traditional Chinese medicine (TCM) in the treatment of disease from the overall perspective.


Assuntos
Antivirais/farmacologia , Betacoronavirus , Infecções por Coronavirus/tratamento farmacológico , Medicamentos de Ervas Chinesas/química , Medicamentos de Ervas Chinesas/farmacologia , Pneumonia Viral/tratamento farmacológico , Mapas de Interação de Proteínas/efeitos dos fármacos , Antivirais/química , Cápsulas/farmacologia , Caspase 3/genética , Caspase 8/genética , Infecções por Coronavirus/genética , Expressão Gênica/efeitos dos fármacos , Humanos , Interleucina-6/genética , Proteína Quinase 1 Ativada por Mitógeno/genética , Pandemias , Pneumonia Viral/genética , Mapas de Interação de Proteínas/genética , Fator de Transcrição RelA/genética
2.
BMC Bioinformatics ; 21(Suppl 14): 368, 2020 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-32998690

RESUMO

BACKGROUND: Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patient-based biomarkers which are the candidates of molecular targeting. RESULTS: We built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories. We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets. CONCLUSIONS: This 12-gene signature could predict the prognostic risk of the patients with lung adenocarcinoma in TCGA and they are potential predictors for the survival-based risk clustering of the patients with lung adenocarcinoma. These genes can be used to cluster patients based on molecular nature and the best candidates of drugs for the patient clusters can be proposed. These genes also have a high potential for targeted cancer therapy of patients with lung adenocarcinoma.


Assuntos
Adenocarcinoma de Pulmão/patologia , Genômica/métodos , Neoplasias Pulmonares/patologia , Transcriptoma , Adenocarcinoma de Pulmão/genética , Adenocarcinoma de Pulmão/mortalidade , Área Sob a Curva , Análise por Conglomerados , Variações do Número de Cópias de DNA , Bases de Dados Genéticas , Regulação Neoplásica da Expressão Gênica , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/mortalidade , Estadiamento de Neoplasias , Prognóstico , Modelos de Riscos Proporcionais , Mapas de Interação de Proteínas/genética , Curva ROC , Fatores de Risco , Taxa de Sobrevida
3.
BMC Bioinformatics ; 21(Suppl 14): 359, 2020 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-32998692

RESUMO

BACKGROUND: The abundance of molecular profiling of breast cancer tissues entailed active research on molecular marker-based early diagnosis of metastasis. Recently there is a surging interest in combining gene expression with gene networks such as protein-protein interaction (PPI) network, gene co-expression (CE) network and pathway information to identify robust and accurate biomarkers for metastasis prediction, reflecting the common belief that cancer is a systems biology disease. However, controversy exists in the literature regarding whether network markers are indeed better features than genes alone for predicting as well as understanding metastasis. We believe much of the existing results may have been biased by the overly complicated prediction algorithms, unfair evaluation, and lack of rigorous statistics. In this study, we propose a simple approach to use network edges as features, based on two types of networks respectively, and compared their prediction power using three classification algorithms and rigorous statistical procedure on one of the largest datasets available. To detect biomarkers that are significant for the prediction and to compare the robustness of different feature types, we propose an unbiased and novel procedure to measure feature importance that eliminates the potential bias from factors such as different sample size, number of features, as well as class distribution. RESULTS: Experimental results reveal that edge-based feature types consistently outperformed gene-based feature type in random forest and logistic regression models under all performance evaluation metrics, while the prediction accuracy of edge-based support vector machine (SVM) model was poorer, due to the larger number of edge features compared to gene features and the lack of feature selection in SVM model. Experimental results also show that edge features are much more robust than gene features and the top biomarkers from edge feature types are statistically more significantly enriched in the biological processes that are well known to be related to breast cancer metastasis. CONCLUSIONS: Overall, this study validates the utility of edge features as biomarkers but also highlights the importance of carefully designed experimental procedures in order to achieve statistically reliable comparison results.


Assuntos
Biomarcadores Tumorais/metabolismo , Neoplasias da Mama/patologia , Máquina de Vetores de Suporte , Área Sob a Curva , Neoplasias da Mama/genética , Feminino , Redes Reguladoras de Genes/genética , Humanos , Modelos Logísticos , Metástase Neoplásica , Mapas de Interação de Proteínas/genética , Curva ROC
4.
BMC Pharmacol Toxicol ; 21(1): 65, 2020 09 03.
Artigo em Inglês | MEDLINE | ID: mdl-32883368

RESUMO

BACKGROUND: Severe acute respiratory syndrome coronavirus (SARS-CoV-2), an emerging Betacoronavirus, is the causative agent of COVID-19. Angiotensin converting enzyme 2 (ACE2), being the main cell receptor of SARS-CoV-2, plays a role in the entry of the virus into the cell. Currently, there are neither specific antiviral drugs for the treatment or preventive drugs such as vaccines. METHODS: We proposed a bioinformatics analysis to test in silico existing drugs as a fast way to identify an efficient therapy. We performed a differential expression analysis in order to identify differentially expressed genes in COVID-19 patients correlated with ACE-2 and we explored their direct relations with a network approach integrating also drug-gene interactions. The drugs with a central role in the network were also investigated with a molecular docking analysis. RESULTS: We found 825 differentially expressed genes correlated with ACE2. The protein-protein interactions among differentially expressed genes identified a network of 474 genes and 1130 interactions. CONCLUSIONS: The integration of drug-gene interactions in the network and molecular docking analysis allows us to obtain several drugs with antiviral activity that, alone or in combination with other treatment options, could be considered as therapeutic approaches against COVID-19.


Assuntos
Antivirais/análise , Antivirais/farmacologia , Betacoronavirus/efeitos dos fármacos , Infecções por Coronavirus/tratamento farmacológico , Infecções por Coronavirus/genética , Reposicionamento de Medicamentos , Pneumonia Viral/tratamento farmacológico , Pneumonia Viral/genética , Mapas de Interação de Proteínas/genética , Antivirais/uso terapêutico , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/virologia , Regulação da Expressão Gênica , Humanos , Simulação de Acoplamento Molecular , Terapia de Alvo Molecular , Pandemias/prevenção & controle , Peptidil Dipeptidase A/genética , Pneumonia Viral/prevenção & controle , Pneumonia Viral/virologia
5.
PLoS Comput Biol ; 16(9): e1008229, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32936825

RESUMO

Accurately predicting essential genes using computational methods can greatly reduce the effort in finding them via wet experiments at both time and resource scales, and further accelerate the process of drug discovery. Several computational methods have been proposed for predicting essential genes in model organisms by integrating multiple biological data sources either via centrality measures or machine learning based methods. However, the methods aiming to predict human essential genes are still limited and the performance still need improve. In addition, most of the machine learning based essential gene prediction methods are lack of skills to handle the imbalanced learning issue inherent in the essential gene prediction problem, which might be one factor affecting their performance. We propose a deep learning based method, DeepHE, to predict human essential genes by integrating features derived from sequence data and protein-protein interaction (PPI) network. A deep learning based network embedding method is utilized to automatically learn features from PPI network. In addition, 89 sequence features were derived from DNA sequence and protein sequence for each gene. These two types of features are integrated to train a multilayer neural network. A cost-sensitive technique is used to address the imbalanced learning problem when training the deep neural network. The experimental results for predicting human essential genes show that our proposed method, DeepHE, can accurately predict human gene essentiality with an average performance of AUC higher than 94%, the area under precision-recall curve (AP) higher than 90%, and the accuracy higher than 90%. We also compare DeepHE with several widely used traditional machine learning models (SVM, Naïve Bayes, Random Forest, and Adaboost) using the same features and utilizing the same cost-sensitive technique to against the imbalanced learning issue. The experimental results show that DeepHE significantly outperforms the compared machine learning models. We have demonstrated that human essential genes can be accurately predicted by designing effective machine learning algorithm and integrating representative features captured from available biological data. The proposed deep learning framework is effective for such task.


Assuntos
Biologia Computacional/métodos , Aprendizado Profundo , Genes Essenciais/genética , Análise de Sequência de DNA/métodos , DNA/genética , Humanos , Redes Neurais de Computação , Mapas de Interação de Proteínas/genética
6.
BMC Infect Dis ; 20(1): 612, 2020 Aug 18.
Artigo em Inglês | MEDLINE | ID: mdl-32811479

RESUMO

BACKGROUND: Pulmonary tuberculosis (PTB) is one of the serious infectious diseases worldwide; however, the gene network involved in the host response remain largely unclear. METHODS: This study integrated two cohorts profile datasets GSE34608 and GSE83456 to elucidate the potential gene network and signaling pathways in PTB. Differentially expressed genes (DEGs) were obtained for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using Metascape database. Protein-Protein Interaction (PPI) network of DEGs was constructed by the online database the Search Tool for the Retrieval of Interacting Genes (STRING). Modules were identified by the plug-in APP Molecular Complex Detection (MCODE) in Cytoscape. GO and KEGG pathway of Module 1 were further analyzed by STRING. Hub genes were selected for further expression validation in dataset GSE19439. The gene expression level was also investigated in the dataset GSE31348 to display the change pattern during the PTB treatment. RESULTS: Totally, 180 shared DEGs were identified from two datasets. Gene function and KEGG pathway enrichment revealed that DEGs mainly enriched in defense response to other organism, response to bacterium, myeloid leukocyte activation, cytokine production, etc. Seven modules were clustered based on PPI network. Module 1 contained 35 genes related to cytokine associated functions, among which 14 genes, including chemokine receptors, interferon-induced proteins and Toll-like receptors, were identified as hub genes. Expression levels of the hub genes were validated with a third dataset GSE19439. The signature of this core gene network showed significant response to Mycobacterium tuberculosis (Mtb) infection, and correlated with the gene network pattern during anti-PTB therapy. CONCLUSIONS: Our study unveils the coordination of causal genes during PTB infection, and provides a promising gene panel for PTB diagnosis. As major regulators of the host immune response to Mtb infection, the 14 hub genes are also potential molecular targets for developing PTB drugs.


Assuntos
Biologia Computacional/métodos , Redes Reguladoras de Genes , Mycobacterium tuberculosis , Mapas de Interação de Proteínas/genética , Transcriptoma , Tuberculose Pulmonar/genética , Biomarcadores , Estudos de Coortes , Perfilação da Expressão Gênica , Regulação da Expressão Gênica/efeitos dos fármacos , Humanos , Transdução de Sinais/genética , Tuberculose Pulmonar/diagnóstico , Tuberculose Pulmonar/tratamento farmacológico , Tuberculose Pulmonar/microbiologia
7.
Medicine (Baltimore) ; 99(32): e21702, 2020 Aug 07.
Artigo em Inglês | MEDLINE | ID: mdl-32769939

RESUMO

Hepatocellular carcinoma (HCC) is a malignant tumor with unsatisfactory prognosis. The abnormal genes expression is significantly associated with initiation and poor prognosis of HCC. The aim of the present study was to identify molecular biomarkers related to the initiation and development of HCC via bioinformatics analysis, so as to provide a certain molecular mechanism for individualized treatment of hepatocellular carcinoma.Three datasets (GSE101685, GSE112790, and GSE121248) from the GEO database were used for the bioinformatics analysis. Differentially expressed genes (DEGs) of HCC and normal liver samples were obtained using GEO2R online tools. Gene ontology term and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were conducted via the Database for Annotation, Visualization, and Integrated Discovery online bioinformatics tool. The protein-protein interaction (PPI) network was constructed by the Search Tool for the Retrieval of Interacting Genes database and hub genes were visualized by Cytoscape. Survival analysis and RNA sequencing expression were conducted by UALCAN and Gene Expression Profiling Interactive Analysis.A total of 115 shared DEGs were identified, including 30 upregulated genes and 85 downregulated genes in HCC samples. P53 signaling pathway and cell cycle were the major enriched pathways for the upregulated DEGs whereas metabolism-related pathways were the major enriched pathways for the downregulated DEGs. The PPI network was established with 105 nodes and 249 edges and 3 significant modules were identified via molecular complex detection. Additionally, 17 candidate genes from these 3 modules were significantly correlated with HCC patient survival and 15 of 17 genes exhibited high expression level in HCC samples. Moreover, 4 hub genes (CCNB1, CDK1, RRM2, BUB1B) were identified for further reanalysis of KEGG pathway, and enriched in 2 pathways, the P53 signaling pathway and cell cycle pathway.Overexpression of CCNB1, CDK1, RRM2, and BUB1B in HCC samples was correlated with poor survival in HCC patients, which could be potential therapeutic targets for HCC.


Assuntos
Biomarcadores Tumorais/análise , Carcinoma Hepatocelular/diagnóstico , Biologia Computacional/estatística & dados numéricos , Programas de Rastreamento/normas , Prognóstico , Carcinoma Hepatocelular/mortalidade , Carcinoma Hepatocelular/fisiopatologia , China/epidemiologia , Análise por Conglomerados , Expressão Gênica/genética , Humanos , Neoplasias Hepáticas/epidemiologia , Neoplasias Hepáticas/mortalidade , Neoplasias Hepáticas/patologia , Programas de Rastreamento/métodos , Mapas de Interação de Proteínas/genética , Análise de Sobrevida
8.
Medicine (Baltimore) ; 99(34): e21863, 2020 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-32846838

RESUMO

Dermatomyositis is a common connective tissue disease. The occurrence and development of dermatomyositis is a result of multiple factors, but its exact pathogenesis has not been fully elucidated. Here, we used biological information method to explore and predict the major disease related genes of dermatomyositis and to find the underlying pathogenic molecular mechanism.The gene expression data of GDS1956, GDS2153, GDS2855, and GDS3417 including 94 specimens, 66 cases of dermatomyositis specimens and 28 cases of normal specimens, were obtained from the Gene Expression Omnibus database. The 4 microarray gene data groups were combined to get differentially expressed genes (DEGs). The gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were operated by the database for annotation, visualization and integrated discovery and KEGG orthology based annotation system databases, separately. The protein-protein interaction networks of the DEGs were built from the STRING website. A total of 4097 DEGs were extracted from the 4 Gene Expression Omnibus datasets, of which 2213 genes were upregulated, and 1884 genes were downregulated. Gene ontology analysis indicated that the biological functions of DEGs focused primarily on response to virus, type I interferon signaling pathway and negative regulation of viral genome replication. The main cellular components include extracellular space, cytoplasm, and blood microparticle. The molecular functions include protein binding, double-stranded RNA binding and MHC class I protein binding. KEGG pathway analysis showed that these DEGs were mainly involved in the toll-like receptor signaling pathway, cytosolic DNA-sensing pathway, RIG-I-like receptor signaling pathway, complement and coagulation cascades, arginine and proline metabolism, phagosome signaling pathway. The following 13 closely related genes, XAF1, NT5E, UGCG, GBP2, TLR3, DDX58, STAT1, GBP1, PLSCR1, OAS3, SP100, IGK, and RSAD2, were key nodes from the protein-protein interaction network.This research suggests that exploring for DEGs and pathways in dermatomyositis using integrated bioinformatics methods could help us realize the molecular mechanism underlying the development of dermatomyositis, be of actual implication for the early detection and prophylaxis of dermatomyositis and afford reliable goals for the curing of dermatomyositis.


Assuntos
Biologia Computacional/instrumentação , Dermatomiosite/genética , Ontologia Genética/tendências , Interferon Tipo I/genética , Mapas de Interação de Proteínas/genética , Dermatomiosite/epidemiologia , Motivo de Ligação ao RNA de Cadeia Dupla/genética , Regulação para Baixo , Humanos , Incidência , Análise em Microsséries/métodos , Anotação de Sequência Molecular/métodos , Ligação Proteica , Transdução de Sinais , Regulação para Cima
9.
PLoS Genet ; 16(8): e1008996, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32841242

RESUMO

The utilization of different carbon sources in filamentous fungi underlies a complex regulatory network governed by signaling events of different protein kinase pathways, including the high osmolarity glycerol (HOG) and protein kinase A (PKA) pathways. This work unraveled cross-talk events between these pathways in governing the utilization of preferred (glucose) and non-preferred (xylan, xylose) carbon sources in the reference fungus Aspergillus nidulans. An initial screening of a library of 103 non-essential protein kinase (NPK) deletion strains identified several mitogen-activated protein kinases (MAPKs) to be important for carbon catabolite repression (CCR). We selected the MAPKs Ste7, MpkB, and PbsA for further characterization and show that they are pivotal for HOG pathway activation, PKA activity, CCR via regulation of CreA cellular localization and protein accumulation, as well as for hydrolytic enzyme secretion. Protein-protein interaction studies show that Ste7, MpkB, and PbsA are part of the same protein complex that regulates CreA cellular localization in the presence of xylan and that this complex dissociates upon the addition of glucose, thus allowing CCR to proceed. Glycogen synthase kinase (GSK) A was also identified as part of this protein complex and shown to potentially phosphorylate two serine residues of the HOG MAPKK PbsA. This work shows that carbon source utilization is subject to cross-talk regulation by protein kinases of different signaling pathways. Furthermore, this study provides a model where the correct integration of PKA, HOG, and GSK signaling events are required for the utilization of different carbon sources.


Assuntos
Proteínas Quinases Dependentes de AMP Cíclico/genética , Glucose/metabolismo , Quinases da Glicogênio Sintase/genética , Proteínas Quinases Ativadas por Mitógeno/genética , Aspergillus nidulans/enzimologia , Repressão Catabólica/genética , Fungos/genética , Fungos/metabolismo , Glicerol/metabolismo , Concentração Osmolar , Fosforilação/genética , Mapas de Interação de Proteínas/genética , Proteínas Repressoras/genética , Xilose/metabolismo
10.
Eur Rev Med Pharmacol Sci ; 24(13): 7497-7505, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32706090

RESUMO

OBJECTIVE: The specific mechanism of cytokine storm in COVID-19 infected patients is not clear. This study aims to identify the key genes that cause cytokine storm in COVID-19 infected patients. MATERIALS AND METHODS: We conducted a difference analysis on the GSE147507 data set. The analysis results are combined with immune genes to obtain immune-related genes among the differential genes. Finally, GO enrichment analysis, PPI analysis, core gene identification, and ssGSEA enrichment analysis were performed on the new gene set. RESULTS: A total of 232 differential genes were screened out. After merging with immune genes, a total of 29 immune-related genes were obtained. Further analysis revealed that the genes were enriched in 16 pathways, and the protein interaction network had a total of 29 nodes and 139 edges. After screening, the core gene was CXCL10. The ssGSEA results of CXCL10 showed that CD4 and CD8 immune-related signature were significantly enriched in high CXCL10 expression, and the samples with low CXCL10 expression were significantly enriched with monocytes and DC immune-related signature. CONCLUSIONS: CXCL10 may be a key gene related to the cytokine storm of COVID-19 infection, and it is expected to become the therapeutic target.


Assuntos
Quimiocina CXCL10/genética , Infecções por Coronavirus/genética , Pneumonia Viral/genética , Betacoronavirus/imunologia , Betacoronavirus/isolamento & purificação , Quimiocina CXCL10/imunologia , Infecções por Coronavirus/imunologia , Humanos , Pandemias , Pneumonia Viral/imunologia , Mapas de Interação de Proteínas/genética , Mapas de Interação de Proteínas/imunologia
11.
BMC Infect Dis ; 20(1): 480, 2020 Jul 06.
Artigo em Inglês | MEDLINE | ID: mdl-32631335

RESUMO

BACKGROUND: Influenza A virus (IAV) infection is a serious public health problem not only in South East Asia but also in European and African countries. Scientists are using network biology to dig deep into the essential host factors responsible for regulation of virus infections. Researchers can explore the virus invasion into the host cells by studying the virus-host relationship based on their protein-protein interaction network. METHODS: In this study, we present a comprehensive IAV-host protein-protein interaction network that is obtained based on the literature-curated protein interaction datasets and some important interaction databases. The network is constructed in Cytoscape and analyzed with its plugins including CytoHubba, CytoCluster, MCODE, ClusterViz and ClusterOne. In addition, Gene Ontology and KEGG enrichment analyses are performed on the highly IAV-associated human proteins. We also compare the current results with those from our previous study on Hepatitis C Virus (HCV)-host protein-protein interaction network in order to find out valuable information. RESULTS: We found out 1027 interactions among 829 proteins of which 14 are viral proteins and 815 belong to human proteins. The viral protein NS1 has the highest number of associations with human proteins followed by NP, PB2 and so on. Among human proteins, LNX2, MEOX2, TFCP2, PRKRA and DVL2 have the most interactions with viral proteins. Based on KEGG pathway enrichment analysis of the highly IAV-associated human proteins, we found out that they are enriched in the KEGG pathway of basal cell carcinoma. Similarly, the result of KEGG analysis of the common host factors involved in IAV and HCV infections shows that these factors are enriched in the infection pathways of Hepatitis B Virus (HBV), Viral Carcinoma, measles and certain other viruses. CONCLUSION: It is concluded that the list of proteins we identified might be used as potential drug targets for the drug design against the infectious diseases caused by Influenza A Virus and other viruses.


Assuntos
Interações Hospedeiro-Patógeno/genética , Vírus da Influenza A/metabolismo , Influenza Humana/metabolismo , Mapas de Interação de Proteínas/genética , Biologia de Sistemas/métodos , Proteínas de Transporte/genética , Proteínas de Ligação a DNA/genética , Hepacivirus/metabolismo , Hepatite C/metabolismo , Hepatite C/virologia , Humanos , Influenza Humana/virologia , Fatores de Transcrição/genética , Proteínas do Core Viral/genética , Proteínas não Estruturais Virais/genética , Replicação Viral
12.
PLoS Genet ; 16(7): e1008903, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32678846

RESUMO

Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named 'core genes', while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer's Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets-consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancer-where we identify 109 candidate core genes.


Assuntos
Doença de Alzheimer/genética , Neoplasias da Mama/genética , Diabetes Mellitus Tipo 2/genética , Estudo de Associação Genômica Ampla , Mapas de Interação de Proteínas/genética , Doença de Alzheimer/patologia , Precursor de Proteína beta-Amiloide/genética , Proteína BRCA1/genética , Neoplasias da Mama/patologia , Diabetes Mellitus Tipo 2/patologia , Feminino , Humanos , Insulina/genética , Polimorfismo de Nucleotídeo Único/genética , Pró-Proteína Convertase 9/genética , Fatores de Risco
13.
Life Sci ; 257: 118039, 2020 Sep 15.
Artigo em Inglês | MEDLINE | ID: mdl-32621925

RESUMO

AIMS: Many studies have demonstrated that circRNAs are closely associated with human diseases. Nonetheless, the potential mechanism by which circRNAs impacts spinal cord injury (SCI) is not fully understood. The aim of this study was to explore the regulatory roles of circRNAs in SCI. MAIN METHODS: The sequencing data of circRNA, miRNA and mRNA were obtained from Gene Expression Omnibus (GEO) datasets. Candidates were identified to construct a circRNA-miRNA-mRNA network based on circRNA-miRNA interactions and miRNA-mRNA interactions. Protein-protein interactions (PPI) analysis was performed to determine hub genes, and a connectivity map (CMap) analysis was applied to determine potential therapeutic targets for SCI. KEY FINDINGS: A total of 1656 differentially expressed circRNAs (DEcircRNAs), 71 differentially expressed miRNAs (DEmiRNAs) and 2782 differentially expressed mRNAs (DEmRNAs) were identified. We integrated four overlapped circRNAs, six miRNAs and 101 target mRNAs into a circRNA-miRNA-mRNA network. We next identified two hub genes (DDIT4, EZR) based on the PPI network and identified five circRNA-miRNA-hub gene regulatory axes. In addition, we discovered three chemicals (tanespimycin, fulvestrant, carbamazepine) as potential treatment options for SCI. SIGNIFICANCE: Our study suggests a regulatory role for circRNAs in the pathogenesis and treatment of SCI from the view of a competitive endogenous RNA (ceRNA) network.


Assuntos
RNA Circular/genética , RNA Circular/fisiologia , Traumatismos da Medula Espinal/genética , Animais , Biologia Computacional/métodos , Bases de Dados Genéticas , Perfilação da Expressão Gênica/métodos , Ontologia Genética , Redes Reguladoras de Genes/genética , Humanos , MicroRNAs/genética , MicroRNAs/fisiologia , Mapas de Interação de Proteínas/genética , RNA Mensageiro/genética , RNA Mensageiro/fisiologia , Ratos
14.
Medicine (Baltimore) ; 99(21): e20404, 2020 May 22.
Artigo em Inglês | MEDLINE | ID: mdl-32481342

RESUMO

BACKGROUND: Peripheral arterial occlusive disease (PAOD) is a global public health concern that decreases the quality of life of the patients and can lead to disabilities and death. The aim of this study was to identify the genes and pathways associated with PAOD pathogenesis, and the potential therapeutic targets. METHODS: Differentially expressed genes (DEGs) and miRNAs related to PAOD were extracted from the GSE57691 dataset and through text mining. Additionally, bioinformatics analysis was applied to explore gene ontology, pathways and protein-protein interaction of those DEGs. The potential miRNAs targeting the DEGs and the transcription factors (TFs) regulating miRNAs were predicted by multiple different databases. RESULTS: A total of 59 DEGs were identified, which were significantly enriched in the inflammatory response, immune response, chemokine-mediated signaling pathway and JAK-STAT signaling pathway. Thirteen genes including IL6, CXCL12, IL1B, and STAT3 were hub genes in protein-protein interaction network. In addition, 513 miRNA-target gene pairs were identified, of which CXCL12 and PTPN11 were the potential targets of miRNA-143, and IL1B of miRNA-21. STAT3 was differentially expressed and regulated 27 potential target miRNAs including miRNA-143 and miRNA-21 in TF-miRNA regulatory network. CONCLUSION: In summary, inflammation, immune response and STAT3-mediated miRNA-target genes axis play an important role in PAOD development and progression.


Assuntos
Simulação por Computador , MicroRNAs/genética , Doença Arterial Periférica/classificação , Doença Arterial Periférica/genética , Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , Humanos , Doença Arterial Periférica/diagnóstico , Mapas de Interação de Proteínas/genética , Fatores de Transcrição/genética
15.
Medicine (Baltimore) ; 99(21): e20268, 2020 May 22.
Artigo em Inglês | MEDLINE | ID: mdl-32481304

RESUMO

BACKGROUND: Ossification of the posterior longitudinal ligament (OPLL) refers to an ectopic ossification disease originating from the posterior longitudinal ligament of the spine. Pressing on the spinal cord or nerve roots can cause limb sensory and motor disorders, significantly reducing the patient's quality of life. At present, the pathogenesis of OPLL is still unclear. The purpose of this study is to integrate microRNA (miRNA)-mRNA biological information data to further analyze the important molecules in the pathogenesis of OPLL, so as to provide targets for future OPLL molecular therapy. METHODS: miRNA and mRNA expression profiles of GSE69787 were downloaded from Gene Expression Omnibus database and analyzed by edge R package. Funrich software was used to predict the target genes and transcription factors of de-miRNA. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of differentially expressed genes (DEGs) were carried out based on CLUEGO plug-in in Cytoscape. Using data collected from a search tool for the retrieval of interacting genes online database, a protein-protein interaction (PPI) network was constructed using Cytoscape. The hub gene selection and module analysis of PPI network were carried out by cytoHubba and molecular complex detection, plug-ins of Cytoscape software respectively. RESULTS: A total of 346 genes, including 247 up-regulated genes and 99 down-regulated genes were selected as DEGs. SP1 was identified as an upstream transcription factor of de-miRNAs. Notably, gene ontology enrichment analysis shows that up- and down-regulated DEGs are mainly involved in BP, such as skeletal structure morphogenesis, skeletal system development, and animal organ morphogenesis. Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that only WNT signaling pathway was associated with osteogenic differentiation. Lymphoid enhancer binding factor 1 and wingless-type MMTV integration site family member 2 Wingless-Type MMTV Integration site family member 2 were identified as hub genes, miR-520d-3p, miR-4782-3p, miR-6766-3p, and miR-199b-5p were identified as key miRNAs. In addition, 2 important network modules were obtained from PPI network. CONCLUSIONS: In this study, we established a potential miRNA-mRNA regulatory network associated with OPLL, revealing the key molecular mechanism of OPLL and providing targets for future treatment or prevent its occurrence.


Assuntos
Biologia Computacional/instrumentação , Fator 1 de Ligação ao Facilitador Linfoide/genética , MicroRNAs/genética , Ossificação do Ligamento Longitudinal Posterior/genética , RNA Mensageiro/genética , Proteína Wnt2/genética , Regulação para Baixo/genética , Perfilação da Expressão Gênica/instrumentação , Ontologia Genética , Redes Reguladoras de Genes/genética , Humanos , Ossificação do Ligamento Longitudinal Posterior/patologia , Ossificação do Ligamento Longitudinal Posterior/fisiopatologia , Ossificação do Ligamento Longitudinal Posterior/psicologia , Osteogênese/genética , Mapas de Interação de Proteínas/genética , Qualidade de Vida , Coluna Vertebral/patologia , Fatores de Transcrição/genética , Regulação para Cima/genética , Via de Sinalização Wnt/genética
16.
BMC Infect Dis ; 20(1): 403, 2020 Jun 09.
Artigo em Inglês | MEDLINE | ID: mdl-32517725

RESUMO

BACKGROUND: Current tools for diagnosing latent TB infection (LTBI) detect immunological memory of past exposure but are unable to determine whether exposure is recent. We sought to identify a whole-blood transcriptome signature of recent TB exposure. METHODS: We studied household contacts of TB patients; healthy volunteers without recent history of TB exposure; and patients with active TB. We performed whole-blood RNA sequencing (in all), an interferon gamma release assay (IGRA; in contacts and healthy controls) and PET/MRI lung scans (in contacts only). We evaluated differentially-expressed genes in household contacts (log2 fold change ≥1 versus healthy controls; false-discovery rate < 0.05); compared these to differentially-expressed genes seen in the active TB group; and assessed the association of a composite gene expression score to independent exposure/treatment/immunological variables. RESULTS: There were 186 differentially-expressed genes in household contacts (n = 26, age 22-66, 46% male) compared with healthy controls (n = 5, age 29-38, 100% male). Of these genes, 141 (76%) were also differentially expressed in active TB (n = 14, age 27-69, 71% male). The exposure signature included genes from inflammatory response, type I interferon signalling and neutrophil-mediated immunity pathways; and genes such as BATF2 and SCARF1 known to be associated with incipient TB. The composite gene-expression score was higher in IGRA-positive contacts (P = 0.04) but not related to time from exposure, isoniazid prophylaxis, or abnormalities on PET/MRI (all P > 0.19). CONCLUSIONS: Transcriptomics can detect TB exposure and, with further development, may be an approach of value for epidemiological research and targeting public health interventions.


Assuntos
Tuberculose Latente/diagnóstico , RNA/sangue , Adulto , Idoso , Fatores de Transcrição de Zíper de Leucina Básica/genética , Estudos de Casos e Controles , Busca de Comunicante , Feminino , Humanos , Interferon Tipo I/metabolismo , Tuberculose Latente/microbiologia , Tuberculose Latente/transmissão , Masculino , Pessoa de Meia-Idade , Neutrófilos/imunologia , Neutrófilos/metabolismo , Mapas de Interação de Proteínas/genética , RNA/química , RNA/metabolismo , Receptores Depuradores Classe F/genética , Proteínas Supressoras de Tumor/genética , Adulto Jovem
17.
J Cancer Res Clin Oncol ; 146(9): 2299-2310, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32556504

RESUMO

BACKGROUND: Although much progress has been made in the diagnosis of early-stage lung adenocarcinoma (ES-LUAD), the prognosis for ES-LUAD patients with rapid recurrence is still poor. Importantly, there is currently no effective and precise method to screen patients who may develop rapid recurrence. Therefore, it is necessary to identify potential differentially expressed genes (DEGs) in ES-LUAD patients with rapid recurrence and non-rapid recurrence. METHODS: Affymetrix GeneChip Human Transcriptome Array was used to identify DEGs between ES-LUAD patients with rapid recurrence and non-rapid recurrence. Rapid recurrence was defined as recurrence-free survival (RFS) â‰¦ 1 year and non-rapid recurrence was defined as RFS â‰§ 3 years. The biological functions of the DEGs were analyzed by GO and KEGG pathway enrichment analyses. The protein-protein interaction (PPI) network of identified DEGs was conducted by STRING and Cytoscape software. The expression level of crucial hub genes and tumor-infiltrating lymphocytes (TILs) was verified by immunohistochemistry (IHC). RESULTS: A total of 416 DEGs were identified between ES-LUAD patients with and without rapid recurrence. The results of GO analysis revealed that 2 of the top 10 categories in the domain of cellular component, 2 of the top 10 in the domain of molecular function, and 9 of the top 10 in the domain of biological process were functionally related to immunity. The results of KEGG analysis showed that 6 of the top 8 pathways were functionally involved in immune regulation and inflammatory response. The PPI network analysis identified ten crucial nodal protein, including EGFR, MMP9, IL-1ß, PTGS2, MMP1, and 5 histone proteins, which constituted 25 key interactions. IL-1ß and PTGS2 expression were closely related to immunity and IHC analysis further revealed that low expression of IL-1ß and PTGS2 is associated with rapid recurrence. Kaplan-Meier analysis further revealed that LUAD patients with lower IL-1ß or PTGS2 expression had a worse RFS. When the TIL density of CD3+, CD4+, CD8+ and CD20+ subsets was less than 20%, ES-LUAD patients have a higher probability of rapid recurrence. CONCLUSION: There were significant differences in the expression of immune-related genes between patients with rapid recurrence and patient with non-rapid recurrence. Immune-related genes such as IL-1ß and PTGS2 and TIL density (20%) play important roles in rapid recurrence of ES-LUAD. This study provided a theoretical basis for distinguishing the two types of patients from an immunological perspective.


Assuntos
Adenocarcinoma de Pulmão/genética , Neoplasias Pulmonares/genética , Recidiva Local de Neoplasia/genética , Adenocarcinoma de Pulmão/patologia , Biomarcadores Tumorais/genética , Biologia Computacional/métodos , Intervalo Livre de Doença , Feminino , Perfilação da Expressão Gênica/métodos , Regulação Neoplásica da Expressão Gênica/genética , Ontologia Genética , Redes Reguladoras de Genes/genética , Humanos , Imuno-Histoquímica/métodos , Estimativa de Kaplan-Meier , Neoplasias Pulmonares/patologia , Masculino , Pessoa de Meia-Idade , Recidiva Local de Neoplasia/patologia , Prognóstico , Mapas de Interação de Proteínas/genética , Transcriptoma/genética
18.
PLoS Genet ; 16(6): e1008881, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32525871

RESUMO

Iron is an essential nutrient required as a cofactor for many biological processes. As a fungal commensal-pathogen of humans, Candida albicans encounters a range of bioavailable iron levels in the human host and maintains homeostasis with a conserved regulatory circuit. How C. albicans senses and responds to iron availability is unknown. In model yeasts, regulation of the iron homeostasis circuit requires monothiol glutaredoxins (Grxs), but their functions beyond the regulatory circuit are unclear. Here, we show Grx3 is required for virulence and growth on low iron for C. albicans. To explore the global roles of Grx3, we applied a proteomic approach and performed in vivo cross-linked tandem affinity purification coupled with mass spectrometry. We identified a large number of Grx3 interacting proteins that function in diverse biological processes. This included Fra1 and Bol2/Fra2, which function with Grxs in intracellular iron trafficking in other organisms. Grx3 interacts with and regulates the activity of Sfu1 and Hap43, components of the C. albicans iron regulatory circuit. Unlike the regulatory circuit, which determines expression or repression of target genes in response to iron availability, Grx3 amplifies levels of gene expression or repression. Consistent with the proteomic data, the grx3 mutant is sensitive to heat shock, oxidative, nitrosative, and genotoxic stresses, and shows growth dependence on histidine, leucine, and tryptophan. We suggest Grx3 is a conserved global regulator of iron-dependent processes occurring within the cell.


Assuntos
Candida albicans/fisiologia , Candidíase Invasiva/microbiologia , Proteínas Fúngicas/metabolismo , Glutarredoxinas/metabolismo , Ferro/metabolismo , Animais , Candida albicans/patogenicidade , Modelos Animais de Doenças , Proteínas Fúngicas/genética , Proteínas Fúngicas/isolamento & purificação , Fatores de Transcrição GATA/metabolismo , Regulação Fúngica da Expressão Gênica , Glutarredoxinas/genética , Glutarredoxinas/isolamento & purificação , Homeostase , Humanos , Hifas , Masculino , Camundongos , Mutação , Mapeamento de Interação de Proteínas , Mapas de Interação de Proteínas/genética , Proteômica , Virulência/genética
19.
J Vis Exp ; (159)2020 05 17.
Artigo em Inglês | MEDLINE | ID: mdl-32478742

RESUMO

Proximity labeling (PL) techniques using engineered ascorbate peroxidase (APEX) or Escherichia coli biotin ligase BirA (known as BioID) have been successfully used for identification of protein-protein interactions (PPIs) in mammalian cells. However, requirements of toxic hydrogen peroxide (H2O2) in APEX-based PL, longer incubation time with biotin (16-24 h), and higher incubation temperature (37 °C) in BioID-based PL severely limit their applications in plants. The recently described TurboID-based PL addresses many limitations of BioID and APEX. TurboID allows rapid proximity labeling of proteins in just 10 min under room temperature (RT) conditions. Although the utility of TurboID has been demonstrated in animal models, we recently showed that TurboID-based PL performs better in plants compared to BioID for labeling of proteins that are proximal to a protein of interest. Provided here is a step-by-step protocol for the identification of protein interaction partners using the N-terminal Toll/interleukin-1 receptor (TIR) domain of the nucleotide-binding leucine-rich repeat (NLR) protein family as a model. The method describes vector construction, agroinfiltration of protein expression constructs, biotin treatment, protein extraction and desalting, quantification, and enrichment of the biotinylated proteins by affinity purification. The protocol described here can be easily adapted to study other proteins of interest in Nicotiana and other plant species.


Assuntos
Plantas/química , Mapas de Interação de Proteínas/genética , Proteômica/métodos , Animais
20.
Science ; 368(6498)2020 06 26.
Artigo em Inglês | MEDLINE | ID: mdl-32586993

RESUMO

Whole-genome duplication has played a central role in the genome evolution of many organisms, including the human genome. Most duplicated genes are eliminated, and factors that influence the retention of persisting duplicates remain poorly understood. We describe a systematic complex genetic interaction analysis with yeast paralogs derived from the whole-genome duplication event. Mapping of digenic interactions for a deletion mutant of each paralog, and of trigenic interactions for the double mutant, provides insight into their roles and a quantitative measure of their functional redundancy. Trigenic interaction analysis distinguishes two classes of paralogs: a more functionally divergent subset and another that retained more functional overlap. Gene feature analysis and modeling suggest that evolutionary trajectories of duplicated genes are dictated by combined functional and structural entanglement factors.


Assuntos
Duplicação Gênica , Genes Duplicados , Genoma Fúngico , Mapas de Interação de Proteínas/genética , Proteínas de Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/genética , Deleção de Genes , Redes Reguladoras de Genes , Técnicas Genéticas , Proteínas de Membrana/genética , Peroxinas/genética
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