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1.
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
2.
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
3.
Nat Commun ; 11(1): 4990, 2020 10 05.
Artigo em Inglês | MEDLINE | ID: mdl-33020478

RESUMO

Neurons are highly compartmentalized cells with tightly controlled subcellular protein organization. While brain transcriptome, connectome and global proteome maps are being generated, system-wide analysis of temporal protein dynamics at the subcellular level are currently lacking. Here, we perform a temporally-resolved surfaceome analysis of primary neuron cultures and reveal dynamic surface protein clusters that reflect the functional requirements during distinct stages of neuronal development. Direct comparison of surface and total protein pools during development and homeostatic synaptic scaling demonstrates system-wide proteostasis-independent remodeling of the neuronal surface, illustrating widespread regulation on the level of surface trafficking. Finally, quantitative analysis of the neuronal surface during chemical long-term potentiation (cLTP) reveals fast externalization of diverse classes of surface proteins beyond the AMPA receptor, providing avenues to investigate the requirement of exocytosis for LTP. Our resource (neurosurfaceome.ethz.ch) highlights the importance of subcellular resolution for systems-level understanding of cellular processes.


Assuntos
Proteínas de Membrana/metabolismo , Plasticidade Neuronal , Neurônios/metabolismo , Sinapses/metabolismo , Animais , Membrana Celular/metabolismo , Células Cultivadas , Potenciais Pós-Sinápticos Excitadores , Homeostase , Potenciação de Longa Duração , Mapas de Interação de Proteínas , Transporte Proteico , Proteostase , Ratos
4.
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
5.
Front Immunol ; 11: 2063, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33013872

RESUMO

Background: Cases of excessive neutrophil counts in the blood in severe coronavirus disease (COVID-19) patients have drawn significant attention. Neutrophil infiltration was also noted on the pathological findings from autopsies. It is urgent to clarify the pathogenesis of neutrophils leading to severe pneumonia in COVID-19. Methods: A retrospective analysis was performed on 55 COVID-19 patients classified as mild (n = 22), moderate (n = 25), and severe (n = 8) according to the Guidelines released by the National Health Commission of China. Trends relating leukocyte counts and lungs examined by chest CT scan were quantified by Bayesian inference. Transcriptional signatures of host immune cells of four COVID19 patients were analyzed by RNA sequencing of lung specimens and BALF. Results: Neutrophilia occurred in 6 of 8 severe patients at 7-19 days after symptom onset, coinciding with lesion progression. Increasing neutrophil counts paralleled lesion CT values (slope: 0.8 and 0.3-1.2), reflecting neutrophilia-induced lung injury in severe patients. Transcriptome analysis revealed that neutrophil activation was correlated with 17 neutrophil extracellular trap (NET)-associated genes in COVID-19 patients, which was related to innate immunity and interacted with T/NK/B cells, as supported by a protein-protein interaction network analysis. Conclusion: Excessive neutrophils and associated NETs could explain the pathogenesis of lung injury in COVID-19 pneumonia.


Assuntos
Betacoronavirus/genética , Infecções por Coronavirus/imunologia , Armadilhas Extracelulares/genética , Ativação de Neutrófilo/genética , Neutrófilos/imunologia , Pneumonia Viral/imunologia , Adulto , Idoso , Teorema de Bayes , Infecções por Coronavirus/virologia , Feminino , Humanos , Contagem de Leucócitos , Lesão Pulmonar/imunologia , Lesão Pulmonar/patologia , Masculino , Pessoa de Meia-Idade , Infiltração de Neutrófilos/imunologia , Pandemias , Pneumonia Viral/virologia , Mapas de Interação de Proteínas/imunologia , RNA Viral/genética , Estudos Retrospectivos , Transcriptoma
6.
Medicine (Baltimore) ; 99(35): e21989, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32871949

RESUMO

BACKGROUND: Bipolar disorder (BD), a common kind of mood disorder with frequent recurrence, high rates of additional comorbid conditions and poor compliance, has an unclear pathogenesis. The Gene Expression Omnibus (GEO) database is a gene expression database created and maintained by the National Center for Biotechnology Information. Researchers can download expression data online for bioinformatics analysis, especially for cancer research. However, there is little research on the use of such bioinformatics analysis methodologies for mental illness by downloading differential expression data from the GEO database. METHODS: Publicly available data were downloaded from the GEO database (GSE12649, GSE5388 and GSE5389), and differentially expressed genes (DEGs) were extracted by using the online tool GEO2R. A Venn diagram was used to screen out common DEGs between postmortem brain tissues and normal tissues. Functional annotation and pathway enrichment analysis of DEGs were performed by using Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses, respectively. Furthermore, a protein-protein interaction network was constructed to identify hub genes. RESULTS: A total of 289 DEGs were found, among which 5 of 10 hub genes [HSP90AA1, HSP90AB 1, UBE2N, UBE3A, and CUL1] were identified as susceptibility genes whose expression was downregulated. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that variations in these 5 hub genes were obviously enriched in protein folding, protein polyubiquitination, apoptotic process, protein binding, the ubiquitin-mediated proteolysis pathway, and protein processing in the endoplasmic reticulum pathway. These findings strongly suggested that HSP90AA1, UBE3A, and CUL 1, which had large areas under the curve in receiver operator curves (P < .05), were potential diagnostic markers for BD. CONCLUSION: Although there are 3 hub genes [HSP90AA1, UBE3A, and CUL 1] that are tightly correlated with the occurrence of BD, mainly based on routine bioinformatics methods for cancer-related disease, the feasibility of applying this single GEO bioinformatics approach for mental illness is questionable, given the significant differences between mental illness and cancer-related diseases.


Assuntos
Transtorno Bipolar/genética , Proteínas Culina/genética , Mineração de Dados/métodos , Proteínas de Choque Térmico HSP90/genética , Ubiquitina-Proteína Ligases/genética , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Feminino , Predisposição Genética para Doença , Humanos , Lactente , Masculino , Pessoa de Meia-Idade , Neoplasias/genética , Mapas de Interação de Proteínas , Adulto Jovem
7.
Medicine (Baltimore) ; 99(35): e21996, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32871952

RESUMO

It is of significance to evaluate central lymph node status in patients with papillary thyroid carcinoma (PTC), because it can decrease postoperative complications resulting from unnecessary prophylactic central lymph node dissection (CLND). Due to the low sensitivity and specificity of neck ultrasonography in the evaluation of central lymph node metastasis (CLNM), it is urgently required to find alternative biomarkers to predict CLNM in PTC patients, which is the main purpose of this study.RNA-sequencing datasets and clinical data of 506 patients with thyroid carcinoma from the Cancer Genome Atlas (TCGA) database were downloaded and analyzed to identify differentially expressed miRNAs (DEMs), which can independently predict CLNM in PTC. A nomogram predictive of CLNM was developed based on clinical characteristics and the identified miRNAs. Receiver operating characteristics curves were drawn to evaluate the predictive performance of the nomogram. Bioinformatics analyses, including target genes identification, functional enrichment analysis, and protein-protein interaction network, were performed to explore the potential roles of the identified DEMs related to CLNM in PTC.A total of 316 PTC patients were included to identify DEMs. Two hundred thirty-seven (75%) PTC patients were randomly selected from the 316 patients as a training set, while the remaining 79 (25%) patients were regarded as a testing set for validation. Two DEMs, miRNA-146b-3p (HR: 1.327, 95% CI = 1.135-1.551, P = .000) and miRNA-363-3p (HR: 0.714, 95% CI = 0.528-0.966, P = .029), were significantly associated with CLNM. A risk score based on these 2 DEMs and calculating from multivariate logistic regression analysis, was significantly lower in N0 group over N1a group in both training (N0 vs N1a: 2.04 ±â€Š1.01 vs 2.73 ±â€Š0.61, P = .000) and testing (N0 vs N1a: 2.20 ±â€Š0.93 vs 2.79 ±â€Š0.68, P = .003) sets. The nomogram including risk score, age, and extrathyroidal extension (ETE) was constructed in the training set and was then validated in the testing set, which showed better prediction value than the other three predictors (risk score, age, and ETE) in terms of CLNM identification. Bioinformatics analyses revealed that 5 hub genes, SLC6A1, SYT1, COL19A1, RIMS2, and COL1A2, might involve in pathways including extracellular matrix organization, ion transmembrane transporter activity, axon guidance, and ABC transporters.On the basis of this study, the nomogram including risk score, age, and ETE showed good prediction of CLNM in PTC, which has a potential to facilitate individualized decision for surgical plans.


Assuntos
Metástase Linfática , MicroRNAs/metabolismo , Nomogramas , Câncer Papilífero da Tireoide/metabolismo , Mineração de Dados , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Mapas de Interação de Proteínas , Câncer Papilífero da Tireoide/genética , Câncer Papilífero da Tireoide/patologia
8.
Medicine (Baltimore) ; 99(35): e21997, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32871953

RESUMO

BACKGROUND: Ulcerative colitis (UC) was a type of inflammatory bowel diseases, which was difficult to cure and even would malignant turn into colon cancer. The specific etiology and molecular mechanism of UC were unclear to date. The purpose of this study was to search for new targets for the diagnosis and treatment of UC. METHODS: Firstly, we downloaded the gene expression data of UC from the gene expression omnibus database database (GSE107499), and used multiple bioinformatics methods to find differently expressed genes (DEGs) in UC. Subsequently, we evaluated the lymphocyte infiltration in UC inflamed colon tissue by using the cell type identification by estimating relative subset of known RNA transcripts method. RESULTS: We obtained 1175 DEGs and 8 hub genes (IL6, TNF, PTPRC, CXCL8, FN1, CD44, IL1B, and MMP9) in this study. Among them, 903 DEGs were up-regulated and 272 DEGs were down-regulated. Compared with non-inflamed colon tissues, the inflamed colon tissues had higher levels of memory B cells, activated memory CD4 T cells, follicular helper T cells, M1 macrophages, resting dendritic cells, activated dendritic cells, activated mast cells, and neutrophils, whereas the proportions of plasma cells, resting memory CD4 T cells, gamma delta T cells, activated NK cells, M2 macrophages and resting mast cells were relatively lower. CONCLUSIONS: The DEGs, hub genes and different lymphatic infiltration conditions can provide new targets for diagnosis and treatment of UC. However, these were just predictions through some theoretical methods, and more basic experiments will be needed to prove in the future.


Assuntos
Colite Ulcerativa/metabolismo , Linfócitos/fisiologia , Colite Ulcerativa/genética , Colite Ulcerativa/imunologia , Biologia Computacional , Humanos , Mapas de Interação de Proteínas , Transcriptoma
9.
Medicine (Baltimore) ; 99(35): e22047, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32871963

RESUMO

BACKGROUND: We identified the hub genes and pathways dysregulated in acute myeloid leukemia and the potential molecular mechanisms involved. METHODS: We downloaded the GSE15061 gene expression dataset from the Gene Expression Omnibus database and used weighted gene co-expression network analysis to identify hub genes. Differential expression of the genes was evaluated using the limma package in R software. Subsequently, we built a protein-protein interaction network followed by functional enrichment analysis. Then, the prognostic significance of gene expression was explored in terms of overall survival. Finally, transcription factor-mRNA (ribonucleic acid) and microRNA-mRNA interaction analysis was also explored. RESULTS: We identified 100 differentially expressed hub genes. Functional enrichment analysis indicated that the genes were principally involved in immune system regulation, host defense, and negative regulation of apoptosis and myeloid cell differentiation. We identified 4 hub genes, the expression of which was significantly correlated with overall survival. Finally, 26 key regulators for hub genes and 38 microRNA-mRNA interactions were identified. CONCLUSION: We performed a comprehensive bioinformatics analysis of hub genes potentially involved in acute myeloid leukemia development. Further molecular biological experiments are required to confirm the roles played by these genes.


Assuntos
Leucemia Mieloide Aguda/metabolismo , Biologia Computacional , Humanos , Leucemia Mieloide Aguda/genética , Leucemia Mieloide Aguda/mortalidade , MicroRNAs/metabolismo , Mapas de Interação de Proteínas , Análise de Sobrevida , Fatores de Transcrição/metabolismo , Transcriptoma
10.
Medicine (Baltimore) ; 99(33): e21707, 2020 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-32872047

RESUMO

Osteoarthritis (OA) is a chronic degenerative joint disease with its onset closely related to the growth of synovial fibroblasts (SFs), yet the genes involved in are few reported. In our study, we aimed to identify the OA-associated key gene and pathways via the single-cell RNA sequencing (scRNA-seq) analysis on SFs.scRNA-seq data of SFs from OA sufferers were accessed from GEO database, then the genes involved in were subjected to principal component analysis (PCA) and T-Stochastic Neighbor Embedding (TSNE) Analysis. GO and KEGG enrichment analyses were performed to find the most enriched functions and pathways associated with marker genes and a PPI network was constructed to identify the key gene associated with OA occurrence.Findings revealed that marker genes in three cell types identified by TSNE were mainly activated in pathways firmly related to fibroblasts growth, such as extracellular matrix, immune and cell adhesion molecule binding-associated functions and pathways. Moreover, fibronectin1 (FN1) was validated as the key gene that was tightly related to the growth of SFs, as well as had the potential to play a key role in OA occurrence.Our study explored the key gene and pathways associated with OA occurrence, which were of great value in further investigation of OA diagnosis as well as pathogenesis.


Assuntos
Fibroblastos/metabolismo , Fibronectinas/genética , Osteoartrite/genética , Humanos , Osteoartrite/metabolismo , Mapas de Interação de Proteínas , Análise de Sequência de RNA , Análise de Célula Única , Membrana Sinovial/citologia
11.
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
12.
Nat Commun ; 11(1): 4845, 2020 09 24.
Artigo em Inglês | MEDLINE | ID: mdl-32973148

RESUMO

Herpesviruses encode conserved protein kinases (CHPKs) to stimulate phosphorylation-sensitive processes during infection. How CHPKs bind to cellular factors and how this impacts their regulatory functions is poorly understood. Here, we use quantitative proteomics to determine cellular interaction partners of human herpesvirus (HHV) CHPKs. We find that CHPKs can target key regulators of transcription and replication. The interaction with Cyclin A and associated factors is identified as a signature of ß-herpesvirus kinases. Cyclin A is recruited via RXL motifs that overlap with nuclear localization signals (NLS) in the non-catalytic N termini. This architecture is conserved in HHV6, HHV7 and rodent cytomegaloviruses. Cyclin A binding competes with NLS function, enabling dynamic changes in CHPK localization and substrate phosphorylation. The cytomegalovirus kinase M97 sequesters Cyclin A in the cytosol, which is essential for viral inhibition of cellular replication. Our data highlight a fine-tuned and physiologically important interplay between a cellular cyclin and viral kinases.


Assuntos
Replicação do DNA/fisiologia , Infecções por Herpesviridae/metabolismo , Herpesviridae/metabolismo , Proteínas Quinases/metabolismo , Animais , Ciclina A/genética , Ciclina A/metabolismo , Citomegalovirus/genética , DNA/metabolismo , Células HEK293 , Herpesviridae/enzimologia , Herpesviridae/genética , Infecções por Herpesviridae/virologia , Humanos , Camundongos , Células NIH 3T3 , Sinais de Localização Nuclear/metabolismo , Fosforilação , Mapas de Interação de Proteínas , Proteínas Virais/genética , Proteínas Virais/metabolismo
13.
Int J Mol Sci ; 21(19)2020 Sep 25.
Artigo em Inglês | MEDLINE | ID: mdl-32993015

RESUMO

The outbreak of a novel coronavirus SARS-CoV-2 responsible for the COVID-19 pandemic has caused a worldwide public health emergency. Due to the constantly evolving nature of the coronaviruses, SARS-CoV-2-mediated alterations on post-transcriptional gene regulations across human tissues remain elusive. In this study, we analyzed publicly available genomic datasets to systematically dissect the crosstalk and dysregulation of the human post-transcriptional regulatory networks governed by RNA-binding proteins (RBPs) and micro-RNAs (miRs) due to SARS-CoV-2 infection. We uncovered that 13 out of 29 SARS-CoV-2-encoded proteins directly interacted with 51 human RBPs, of which the majority of them were abundantly expressed in gonadal tissues and immune cells. We further performed a functional analysis of differentially expressed genes in mock-treated versus SARS-CoV-2-infected lung cells that revealed enrichment for the immune response, cytokine-mediated signaling, and metabolism-associated genes. This study also characterized the alternative splicing events in SARS-CoV-2-infected cells compared to the control, demonstrating that skipped exons and mutually exclusive exons were the most abundant events that potentially contributed to differential outcomes in response to the viral infection. A motif enrichment analysis on the RNA genomic sequence of SARS-CoV-2 clearly revealed the enrichment for RBPs such as SRSFs, PCBPs, ELAVs, and HNRNPs, suggesting the sponging of RBPs by the SARS-CoV-2 genome. A similar analysis to study the interactions of miRs with SARS-CoV-2 revealed functionally important miRs that were highly expressed in immune cells, suggesting that these interactions may contribute to the progression of the viral infection and modulate the host immune response across other human tissues. Given the need to understand the interactions of SARS-CoV-2 with key post-transcriptional regulators in the human genome, this study provided a systematic computational analysis to dissect the role of dysregulated post-transcriptional regulatory networks controlled by RBPs and miRs across tissue types during a SARS-CoV-2 infection.


Assuntos
Betacoronavirus/genética , Betacoronavirus/metabolismo , Infecções por Coronavirus/virologia , Redes Reguladoras de Genes , MicroRNAs/genética , Pneumonia Viral/virologia , Processamento Pós-Transcricional do RNA , Proteínas de Ligação a RNA/metabolismo , Regulação da Expressão Gênica , Genoma Viral , Humanos , MicroRNAs/metabolismo , Pandemias , Mapas de Interação de Proteínas , Proteínas de Ligação a RNA/genética
14.
BMC Bioinformatics ; 21(1): 400, 2020 Sep 10.
Artigo em Inglês | MEDLINE | ID: mdl-32912135

RESUMO

BACKGROUND: Infectious diseases are a cruel assassin with millions of victims around the world each year. Understanding infectious mechanism of viruses is indispensable for their inhibition. One of the best ways of unveiling this mechanism is to investigate the host-pathogen protein-protein interaction network. In this paper we try to disclose many properties of this network. We focus on human as host and integrate experimentally 32,859 interaction between human proteins and virus proteins from several databases. We investigate different properties of human proteins targeted by virus proteins and find that most of them have a considerable high centrality scores in human intra protein-protein interaction network. Investigating human proteins network properties which are targeted by different virus proteins can help us to design multipurpose drugs. RESULTS: As host-pathogen protein-protein interaction network is a bipartite network and centrality measures for this type of networks are scarce, we proposed seven new centrality measures for analyzing bipartite networks. Applying them to different virus strains reveals unrandomness of attack strategies of virus proteins which could help us in drug design hence elevating the quality of life. They could also be used in detecting host essential proteins. Essential proteins are those whose functions are critical for survival of its host. One of the proposed centralities named diversity of predators, outperforms the other existing centralities in terms of detecting essential proteins and could be used as an optimal essential proteins' marker. CONCLUSIONS: Different centralities were applied to analyze human protein-protein interaction network and to detect characteristics of human proteins targeted by virus proteins. Moreover, seven new centralities were proposed to analyze host-pathogen protein-protein interaction network and to detect pathogens' favorite host protein victims. Comparing different centralities in detecting essential proteins reveals that diversity of predator (one of the proposed centralities) is the best essential protein marker.


Assuntos
Interações Hospedeiro-Patógeno , Mapas de Interação de Proteínas , Proteínas/metabolismo , Doenças Transmissíveis/metabolismo , Doenças Transmissíveis/patologia , Doenças Transmissíveis/virologia , Bases de Dados de Proteínas , Humanos , Interface Usuário-Computador , Vírus/patogenicidade
15.
Medicine (Baltimore) ; 99(39): e22172, 2020 Sep 25.
Artigo em Inglês | MEDLINE | ID: mdl-32991410

RESUMO

Osteoporosis is a severe chronic skeletal disorder that increases the risks of disability and mortality; however, the mechanism of this disease and the protein markers for prognosis of osteoporosis have not been well characterized. This study aims to characterize the imbalanced serum proteostasis, the disturbed pathways, and potential serum markers in osteoporosis by using a set of bioinformatic analyses. In the present study, the large-scale proteomics datasets (PXD006464) were adopted from the Proteome Xchange database and processed with MaxQuant. The differentially expressed serum proteins were identified. The biological process and molecular function were analyzed. The protein-protein interactions and subnetwork modules were constructed. The signaling pathways were enriched. We identified 209 upregulated and 230 downregulated serum proteins. The bioinformatic analyses revealed a highly overlapped functional protein classification and the gene ontology terms between the upregulated and downregulated protein groups. Protein-protein interactions and pathway analyses showed a high enrichment in protein synthesis, inflammation, and immune response in the upregulated proteins, and cell adhesion and cytoskeleton regulation in the downregulated proteins. Our findings greatly expand the current view of the roles of serum proteins in osteoporosis and shed light on the understanding of its underlying mechanisms and the discovery of serum proteins as potential markers for the prognosis of osteoporosis.


Assuntos
Mineração de Dados/métodos , Osteoporose/sangue , Proteoma/fisiologia , Biomarcadores , Adesão Celular/fisiologia , Biologia Computacional , Citoesqueleto/metabolismo , Regulação para Baixo , Humanos , Mediadores da Inflamação/metabolismo , Mapas de Interação de Proteínas/fisiologia , Proteômica , Regulação para Cima
16.
DNA Cell Biol ; 39(10): 1895-1906, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32882141

RESUMO

Acute aortic dissection (AD) is one of the most severe and highly mortality vascular disease. Its actual prevalence may be seriously underestimated. We studied different expression genes to understand gene profile change between acute AD and nondiseased individuals, and then discover potential biomarkers and therapeutic targets of acute AD. In our study, acute AD differentially expressed mRNAs and miRNAs were identified through bioinformatics analysis on Gene Expression Omnibus data sets GSE52093, GSE98770, and GSE92427. Then, comprehensive target prediction and network analysis methods were used to evaluate protein-protein interaction networks and to identify Gene Ontology terms for differentially expressed mRNAs. Differentially expressed mRNAs-miRNAs involved in acute AD were assessed as well. Finally, the quantitative real-time PCR and in vitro experiment was used to validate the results. We found Integral Membrane Protein 2C (ITM2C) was low expressed and miR-107-5p was highly expressed in acute AD tissues. Meanwhile, overexpression miR-107-5p promoted the cell proliferation and inhibited the cell apoptosis in RASMC cells. miR-107-5p inhibited the progression of acute AD through targeted ITM2C.


Assuntos
Aneurisma Dissecante/genética , MicroRNAs/genética , Aneurisma Dissecante/metabolismo , Aneurisma Dissecante/patologia , Animais , Apoptose , Biomarcadores/metabolismo , Proliferação de Células , Células Cultivadas , Redes Reguladoras de Genes , Humanos , Proteínas de Membrana/genética , Proteínas de Membrana/metabolismo , MicroRNAs/metabolismo , Miócitos de Músculo Liso/metabolismo , Mapas de Interação de Proteínas , Ratos , Transcriptoma
17.
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
18.
Medicine (Baltimore) ; 99(39): e22105, 2020 Sep 25.
Artigo em Inglês | MEDLINE | ID: mdl-32991406

RESUMO

BACKGROUND: Lymph node metastasis is a significant problem in breast cancer, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism and to explore the key RNAs and pathways that mediate lymph node metastasis in breast cancer. METHODS: GSE100453 and GSE38167 were downloaded from the Gene Expression Omnibus (GEO) database and 569 breast cancer statistics were also downloaded from the TCGA database. Differentially expressed miRNAs were calculated by using R software and GEO2R. Gene ontology and Enriched pathway analysis of target mRNAs were analyzed by using the Database for Database of Annotation Visualization and Integrated Discovery (DAVID) and R software. The protein-protein interaction (PPI) network was performed according to Metascape, String, and Cytoscape software. RESULTS: In total, 6 differentially expressed miRNAs were selected, and 499 mRNAs were identified after filtering. The research of the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that mRNAs enriched in certain tumor pathways. Also, certain hub mRNAs were highlighted after constructed and analyzed the PPI network. A total of 3 out of 6 miRNAs had a significant relationship with the overall survival (P < .05) and showed a good ability of risk prediction model of over survival. CONCLUSIONS: By utilizing bioinformatics analyses, differently expressed miRNAs were identified and constructed a complete gene network. Several potential mechanisms and therapeutic and prognostic targets of lymph node metastasis were also demonstrated in breast cancer.


Assuntos
Neoplasias da Mama/genética , Metástase Linfática/genética , Biologia Computacional , Feminino , Regulação Neoplásica da Expressão Gênica , Redes Reguladoras de Genes , Humanos , MicroRNAs/genética , MicroRNAs/metabolismo , Mapas de Interação de Proteínas , Microambiente Tumoral
19.
Medicine (Baltimore) ; 99(39): e22257, 2020 Sep 25.
Artigo em Inglês | MEDLINE | ID: mdl-32991423

RESUMO

Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer with an increasing incidence. As a pre-cancerous condition, actinic keratosis (AK) has an up to 20% risk of progression to cSCC. This study aims to define the potential genes that associated with genesis and progression of cSCC, thereby further identify critical biomarkers for the prevention, early diagnosis, and effective treatment of cSCC.Two datasets GSE42677 and GSE45216 were downloaded from the GEO. Microarray data analysis was applied to explore the differentially expressed genes (DEGs) between cSCC samples and AK samples. Then functional enrichment analysis, protein-protein interaction (PPI) network, and drug-gene interaction analysis were performed to screen key genes.A total of 711 DEGs, including 238 upregulated genes and 473 downregulated genes, were screened out. DEGs mainly involved in pathways as extracellular matrix (ECM)-receptor interaction, hematopoietic cell lineage, phosphatidylinositol 3-kinase (PI3K-Akt) signaling pathway, and focal adhesion. Candidate genes, including upregulated genes as JUN, filamin A (FLNA), casein kinase 1 delta (CSNK1D), and histone cluster 1 H3 family member f (HIST1H3F), and downregulated genes as androgen receptor (AR), heat shock protein family H member 1 (HSPH1), tropomyosin 1 (TPM1), pyruvate kinase, muscle (PKM), LIM domain and actin binding 1 (LIMA1), and synaptopodin (SYNPO) were screened out. In drug-gene interaction analysis, 13 genes and 44 drugs were identified.This study demonstrates that genes JUN, FLNA, AR, HSPH1, and CSNK1D have the potential to function as targets for diagnosis and treatment of cSCC.


Assuntos
Carcinoma de Células Escamosas/genética , Análise em Microsséries/normas , Neoplasias Cutâneas/genética , Biomarcadores Tumorais/genética , Bases de Dados Genéticas , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Humanos , Ceratose Actínica/genética , Mapas de Interação de Proteínas , Melhoria de Qualidade
20.
Nat Commun ; 11(1): 4509, 2020 09 09.
Artigo em Inglês | MEDLINE | ID: mdl-32908151

RESUMO

Glycolysis is one of the primordial pathways of metabolism, playing a pivotal role in energy metabolism and biosynthesis. Glycolytic enzymes are known to form transient multi-enzyme assemblies. Here we examine the wider protein-protein interactions of plant glycolytic enzymes and reveal a moonlighting role for specific glycolytic enzymes in mediating the co-localization of mitochondria and chloroplasts. Knockout mutation of phosphoglycerate mutase or enolase resulted in a significantly reduced association of the two organelles. We provide evidence that phosphoglycerate mutase and enolase form a substrate-channelling metabolon which is part of a larger complex of proteins including pyruvate kinase. These results alongside a range of genetic complementation experiments are discussed in the context of our current understanding of chloroplast-mitochondrial interactions within photosynthetic eukaryotes.


Assuntos
Proteínas de Arabidopsis/metabolismo , Arabidopsis/fisiologia , Cloroplastos/enzimologia , Glicólise/fisiologia , Mitocôndrias/enzimologia , Arabidopsis/citologia , Proteínas de Arabidopsis/genética , Metabolismo Energético/fisiologia , Mutação , Fosfoglicerato Mutase/genética , Fosfoglicerato Mutase/metabolismo , Fosfopiruvato Hidratase/genética , Fosfopiruvato Hidratase/metabolismo , Fotossíntese/fisiologia , Plantas Geneticamente Modificadas , Mapeamento de Interação de Proteínas , Mapas de Interação de Proteínas/fisiologia , Piruvato Quinase/genética , Piruvato Quinase/metabolismo
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