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2.
Acta Virol ; 63(3): 278-285, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31507193

RESUMO

Dengue virus (DENV) infection is one of the most widely-spread flavivirus infections with no effective antiviral drugs available. Peptide inhibitors have been considered as one of the best drug candidates due to their high specificity, selectivity in their interactions and minimum side effects. In this study, we employed computational studies using YASARA, HADDOCK server and PyMOL software to generate short and linear peptides based on a reference peptide, CP5-46A, to block DENV NS2B-NS3 protease. The inhibition potencies of the peptides were evaluated using in-house DENV2 serine protease and fluorogenic peptide substrates. In vitro analyses were performed to determine the peptides cytotoxicity and the inhibitory effects against DENV2 replication in WRL-68 cells. Our computational analyses revealed that the docking energy of AYA3, a 16 amino acid (aa) (-81.2 ± 10.6 kcal/mol) and AYA9, a 15 aa peptide (-83.8 ± 6.8 kcal/mol) to DENV NS2B-NS3 protease were much lower than the reference peptide (46 aa; -70.9 ± 7.8 kcal/mol) and the standard protease inhibitor, aprotinin (58 aa; -48.2 ± 10.6 kcal/mol). Both peptides showed significant inhibition against DENV2 NS2B-NS3 protease activity with IC50 values of 24 µM and 23 µM, respectively. AYA3 and AYA9 peptides also demonstrated approximately 68% and 83% of viral plaque reduction without significantly affecting cell viability at 50 µM concentration. In short, we generated short linear peptides with lower cytotoxic effect and substantial antiviral activities against DENV2. Further studies are required to investigate the inhibitory effects of these peptides in vivo. Keywords: peptide inhibitors; dengue virus; NS2B-NS3 protease; plaque reduction.


Assuntos
Antivirais , Vírus da Dengue , Peptídeos , Inibidores de Proteases , Replicação Viral , Antivirais/farmacologia , Biologia Computacional , Vírus da Dengue/enzimologia , Ativação Enzimática/efeitos dos fármacos , Humanos , Peptídeos/síntese química , Peptídeos/farmacologia , Inibidores de Proteases/farmacologia , Replicação Viral/efeitos dos fármacos
3.
Gene ; 720: 144088, 2019 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-31476404

RESUMO

BACKGROUND: Secretory leukocyte protease inhibitor (SPLI) was a secreted protein which belongs to a member of whey acidic protein four-disulfide core family. In breast cancer (BC) it may inhibit cell proliferation and promote cancer metastasis. In this study, a comprehensive bioinformatics analysis was performed to identify the expression and prognostic value of SLPI in breast cancer. METHODS: SLPI expression in breast cancer was analyzed in Oncomine online database, which was subsequently confirmed by quantitative PCR (qPCR) in 18 BC samples and western blotting in 26 BC samples. Breast cancer gene-expression miner v4.1 was used to access the expression level with clinicopathological parameters in breast cancer patients. The prognostic values of SLPI in breast cancer were evaluated using the PrognoScan database. RESULTS: Our results indicated that SLPI was downregulated in breast cancer than in normal tissues. SLPI expression was found to be negatively correlated with estrogen receptor (ER) and progesterone receptor (PR) status. SLPI expression level was decreased in negative basal-like status patients compared with positive basal-like status. Meanwhile, triple-negative breast cancer status positive correlated with SLPI. We confirmed a positive correlation between SLPI and interleukin 17 receptor B (IL17RB) express in breast cancer tissues via oncomine co-expression analysis. Ten proteins: Elastase, Granulin, Lipocalin, Defensin beta 103B, Defensin beta 103A, Tubulin, Heparin-binding EGF-like growth factor, Interleukin 6, Epidermal growth factor, Phospholipid scramblase 1 were determinate interactions with SLPI by STRING. CONCLUSION: SLPI could as a biomarker to predict the prognosis values of breast cancer. However, further comprehensive study and mining more evidence are needed to clarify our results.


Assuntos
Biologia Computacional/métodos , Regulação Neoplásica da Expressão Gênica , Inibidor Secretado de Peptidases Leucocitárias/genética , Neoplasias de Mama Triplo Negativas/genética , Adulto , Idoso , Feminino , Humanos , Pessoa de Meia-Idade , Prognóstico , Mapas de Interação de Proteínas , Inibidor Secretado de Peptidases Leucocitárias/metabolismo , Neoplasias de Mama Triplo Negativas/metabolismo , Neoplasias de Mama Triplo Negativas/patologia
4.
Gene ; 720: 144103, 2019 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-31491435

RESUMO

Clear cell renal cell carcinoma (ccRCC) is a highly invasive urological malignant tumor that results in shorter patient survival. At present, the mechanism of ccRCC metastasis is not clear. We explored the possible mechanisms of ccRCC metastasis by analyzing the transcriptome of ccRCC patients from the Cancer Genome Atlas (TCGA) database. Comparing the differences in transcriptome in patients with and without metastasis, we found 323 differential genes (|log2FoldChange| > 1 and P < 0.001). KEGG and GO enrichment analyses of differentially expressed genes (DEGs) suggest that the transfer mechanism of ccRCC may be related to complement and coagulation cascades and cholesterol metabolism. To explore the key genes affecting tumor metastasis, we analyzed the association of these genes with patient survival time and found that 16 genes were significantly associated (P < 0.05). We compared the differences in expression of these 16 genes between ccRCC patients and the normal population, and the results showed that TF and B4GALNT1 were overexpressed in patients. Co-expression gene analysis indicated that TF may participate in the metastasis of cancer through the complement system and mucopolysaccharide biosynthesis. B4GALNT1 may affect metastasis through focal adhesion, calcium signaling pathways, and Hippo signaling pathways. Our studies suggest that the complement system and the coagulation cascade, cholesterol metabolism, calcium pathway and iron transport may be associated in the mechanism of metastasis. TF and B4GALNT1 may be the key genes for metastasis, and they may be potential diagnostic markers and therapeutic targets for ccRCC.


Assuntos
Biomarcadores Tumorais/genética , Carcinoma de Células Renais/genética , Biologia Computacional/métodos , Regulação Neoplásica da Expressão Gênica , Neoplasias Renais/genética , Transcriptoma , Biomarcadores Tumorais/metabolismo , Carcinoma de Células Renais/metabolismo , Carcinoma de Células Renais/secundário , Estudos de Casos e Controles , Feminino , Perfilação da Expressão Gênica , Redes Reguladoras de Genes , Humanos , Neoplasias Renais/metabolismo , Neoplasias Renais/patologia , Masculino , Prognóstico , Mapas de Interação de Proteínas , Transdução de Sinais , Taxa de Sobrevida
5.
Beijing Da Xue Xue Bao Yi Xue Ban ; 51(4): 615-622, 2019 Aug 18.
Artigo em Chinês | MEDLINE | ID: mdl-31420610

RESUMO

OBJECTIVE: To construct the prognostic model and identify the prognostic biomarkers based on long non-coding RNA (lncRNA) in bladder cancer. METHODS: The lncRNA expression data and corresponding clinical data of bladder cancer were collected from The Cancer Genome Atlas (TCGA) database. The software Perl and R, and R packages were used for data integration, extraction, analysis and visualization. Detailly, R package "edgeR" was utilized to screen differentially expressed lncRNA in bladder cancer tissues compared with the normal bladder samples. The univariate Cox regression and the least absolute shrinkage and selection operator (Lasso) regression were performed to identify key lncRNA that were utilized to construct the prognostic model by the multivariate Cox regression. According to the median value of the risk score, all patients were divided into the high-risk group and low-risk group to perform the Kaplan-Meier (K-M) survival curves, receiver operating characteristic (ROC) curve and C-index, estimating the prognostic power of the prognostic model. In addition, the hazard ratio (HR) and 95% confidence interval (CI) of each key lncRNA were also calculated by the multivariate Cox regression. Moreover, we performed the K-M survival analysis for each significant key lncRNA from the result of the multivariate Cox regression. RESULTS: A total of 691 lncRNA were identified as differentially expressed lncRNA, and 35 lncRNA signatures were initially considered associated with the prognosis of bladder cancer, where in 23 lncRNA were identified as key lncRNA associated with the prognosis. The overall survival time in years of the low-risk group was obviously longer than that of the high-risk group [(2.85±2.72) years vs. (1.58±1.51) years, P<0.001]. The area under the ROC curve (AUC) was 0.813 (3-year survival) and 0.778 (5-year survival) respectively, and the C-index was 0.73. In addition, HR and 95%CI of each key lncRNA were calculated by the multivariate Cox regression and 11 lncRNA were significant. Furthermore, K-M survival analysis revealed the independent prognostic value of 3 lncRNA, including AL589765.1 (P=0.004), AC023824.1 (P=0.022)and PKN2-AS1 (P=0.016). CONCLUSION: The present study successfully constructed the prognostic model based on the expression level of 23 lncRNA and finally identified one protective prognostic biomarker AL589765.1, and two adverse prognostic biomarkers including AC023824.1 and PKN2-AS1 in bladder cancer.


Assuntos
Neoplasias da Bexiga Urinária , Biomarcadores Tumorais , Biologia Computacional , Regulação Neoplásica da Expressão Gênica , Humanos , Prognóstico , RNA Longo não Codificante , Neoplasias da Bexiga Urinária/genética
6.
Medicine (Baltimore) ; 98(33): e16807, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31415393

RESUMO

BACKGROUND: Sepsis is a serious clinical condition with a poor prognosis, despite improvements in diagnosis and treatment.Therefore, novel biomarkers are necessary that can help with estimating prognosis and improving clinical outcomes of patients with sepsis. METHODS: The gene expression profiles GSE54514 and GSE63042 were downloaded from the GEO database. DEGs were screened by t test after logarithmization of raw data; then, the common DEGs between the 2 gene expression profiles were identified by up-regulation and down-regulation intersection. The DEGs were analyzed using bioinformatics, and a protein-protein interaction (PPI) survival network was constructed using STRING. Survival curves were constructed to explore the relationship between core genes and the prognosis of sepsis patients based on GSE54514 data. RESULTS: A total of 688 common DEGs were identified between survivors and non-survivors of sepsis, and 96 genes were involved in survival networks. The crucial genes Signal transducer and activator of transcription 5A (STAT5A), CCAAT/enhancer-binding protein beta (CEBPB), Myc proto-oncogene protein (MYC), and REL-associated protein (RELA) were identified and showed increased expression in sepsis survivors. These crucial genes had a positive correlation with patients' survival time according to the survival analysis. CONCLUSIONS: Our findings indicate that the genes STAT5A, CEBPB, MYC, and RELA may be important in predicting the prognosis of sepsis patients.


Assuntos
Proteína beta Intensificadora de Ligação a CCAAT/metabolismo , Proteínas Proto-Oncogênicas c-myc/metabolismo , Fator de Transcrição STAT5/metabolismo , Sepse/genética , Sepse/mortalidade , Fator de Transcrição RelA/metabolismo , Proteínas Supressoras de Tumor/metabolismo , Idoso , Idoso de 80 Anos ou mais , Biologia Computacional , Bases de Dados Genéticas , Regulação para Baixo , Feminino , Marcadores Genéticos , Humanos , Masculino , Pessoa de Meia-Idade , Prognóstico , Mapas de Interação de Proteínas , Fatores de Tempo , Transcriptoma , Regulação para Cima
7.
Zhen Ci Yan Jiu ; 44(6): 424-9, 2019 Jun 25.
Artigo em Chinês | MEDLINE | ID: mdl-31368265

RESUMO

OBJECTIVE: To observe the effect of needling "Danzhong" (CV17), "Zhongwan"(CV12), "Qihai" (CV6), "Xuehai"(SP10)and "Zusanli"(ST36) (Triple Energizer Acupuncture Method) on the learning-memory ability and profile of hippocampal differentially-expressed genes and proteins of SAMP8 mice (rapid aging mice), so as to explore its underlying mechanisms in improving Alzheimer's disease (AD).. METHODS: A total of 60 SAMP8 were used as the dementia model and randomly divided into blank control, acupoint and non-acupoint groups (n=20 per group). The 5 acupuncture points and non-acupoints (subcostal region) on the bilateral sides were needled with filiform needles and manipulated manually for 30 s at each one,once daily, 6 times a week for 4 weeks. The Morris water maze tests (location navigation tests and space probing trials) were used to evaluate the mouse's learning-memory ability. The hippocampal tissue was extracted to detect differentially expressed genes and proteins related to acupuncture intervention by gene chip and isobaric tags for relative and absolute quantitation (iTRAQ) techniques as well as bioinformatic analysis, separately. The information is analyzed through bioinformatics database tools. Finally, immunofluorescence staining was used to verify theresults of microarray analysis. RESULTS: Compared with mice of the control and non-acupoint groups, the escape latency of location navigation task of Morris water maze tests on 4th and 5th day of training was significantly shortened in mice of the acupoint group, and the duration of stay in the original safe-platform quadrant was significantly increased in the acupoint group (P<0.05). Gene microarray displayed that in comparison with the control group, 898 differentially expressed genes were up-regulated, 418 genes were down-regulated in the hippocampus of acupoint group. The iTRAQ analysis indicated that in the acupoint and non-acupoint groups, 286 and 299 differentially expressed proteins were up-regulated, 319 and 179 proteins down-regulated, respectively. Of the 34 terms containing 47 proteins up-regulated by acupoint needling, including intermediate filament, keratin filament, myelin sheath, postsynaptic density, neuron projection were related with neurite and cytoskele-ton. While in the non-acupoint group, of the 24 terms were listed by the system, only the myelin sheath involving 11 differentially expressed proteins functions in activities of neurite and cytoskeleton. Immunofluorescence staining of the hippocampal tissue showed that the high-density distribution areas of neurons and neurite fibers were characterized by decentralization and disordering, with the highlighted areas being mainly near the cell body parts in control mice, but in mice of the acupoint group, the highlighted areas at the neurite were relatively dense, the morphology of hippocampal cells was complete, the fiber structure was clear, dense and orderly, and the neurites were closely arranged and in order, indicating an improvement of the distribution and arrangement of nerve fibers after acupuncture. The height of neurite highlight area of the acupoint group was significantly higher than that of the non-acupoint group (P<0.05).. CONCLUSION: The "Triple Energizer Acupuncture" of acupoints is able to improve the learning-memory ability in SAMP8, which may be related to its effects in regulating the expression and function of hippocampal genes and proteins related to neurite and cytoskeleton.


Assuntos
Terapia por Acupuntura , Doença de Alzheimer , Pontos de Acupuntura , Envelhecimento , Animais , Biologia Computacional , Hipocampo , Camundongos
8.
9.
Stud Health Technol Inform ; 264: 1482-1483, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438192

RESUMO

Drug combination therapy can improve drug efficacy, reduce drug dosage, and overcome drug resistance. Many studies have focused on predicting synergistic drug combinations. However, existing methods fail to consider the heterogeneous characteristics of drugs fully, and it is difficult to identify effective drug combinations. Therefore, we propose a new integrated prediction model based on deep representations by integrating information from multiple domains to accurately and effectively predict drug combinations.


Assuntos
Biologia Computacional , Combinação de Medicamentos
10.
BMC Bioinformatics ; 20(1): 423, 2019 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-31412762

RESUMO

BACKGROUND: Computational drug repositioning, which aims to find new applications for existing drugs, is gaining more attention from the pharmaceutical companies due to its low attrition rate, reduced cost, and shorter timelines for novel drug discovery. Nowadays, a growing number of researchers are utilizing the concept of recommendation systems to answer the question of drug repositioning. Nevertheless, there still lie some challenges to be addressed: 1) Learning ability deficiencies; the adopted model cannot learn a higher level of drug-disease associations from the data. 2) Data sparseness limits the generalization ability of the model. 3)Model is easy to overfit if the effect of negative samples is not taken into consideration. RESULTS: In this study, we propose a novel method for computational drug repositioning, Additional Neural Matrix Factorization (ANMF). The ANMF model makes use of drug-drug similarities and disease-disease similarities to enhance the representation information of drugs and diseases in order to overcome the matter of data sparsity. By means of a variant version of the autoencoder, we were able to uncover the hidden features of both drugs and diseases. The extracted hidden features will then participate in a collaborative filtering process by incorporating the Generalized Matrix Factorization (GMF) method, which will ultimately give birth to a model with a stronger learning ability. Finally, negative sampling techniques are employed to strengthen the training set in order to minimize the likelihood of model overfitting. The experimental results on the Gottlieb and Cdataset datasets show that the performance of the ANMF model outperforms state-of-the-art methods. CONCLUSIONS: Through performance on two real-world datasets, we believe that the proposed model will certainly play a role in answering to the major challenge in drug repositioning, which lies in predicting and choosing new therapeutic indications to prospectively test for a drug of interest.


Assuntos
Algoritmos , Biologia Computacional/métodos , Reposicionamento de Medicamentos , Bases de Dados como Assunto , Descoberta de Drogas , Humanos , Modelos Teóricos , Reprodutibilidade dos Testes
11.
BMC Bioinformatics ; 20(1): 422, 2019 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-31412768

RESUMO

BACKGROUND: One of the main issues in the automated protein function prediction (AFP) problem is the integration of multiple networked data sources. The UNIPred algorithm was thereby proposed to efficiently integrate -in a function-specific fashion- the protein networks by taking into account the imbalance that characterizes protein annotations, and to subsequently predict novel hypotheses about unannotated proteins. UNIPred is publicly available as R code, which might result of limited usage for non-expert users. Moreover, its application requires efforts in the acquisition and preparation of the networks to be integrated. Finally, the UNIPred source code does not handle the visualization of the resulting consensus network, whereas suitable views of the network topology are necessary to explore and interpret existing protein relationships. RESULTS: We address the aforementioned issues by proposing UNIPred-Web, a user-friendly Web tool for the application of the UNIPred algorithm to a variety of biomolecular networks, already supplied by the system, and for the visualization and exploration of protein networks. We support different organisms and different types of networks -e.g., co-expression, shared domains and physical interaction networks. Users are supported in the different phases of the process, ranging from the selection of the networks and the protein function to be predicted, to the navigation of the integrated network. The system also supports the upload of user-defined protein networks. The vertex-centric and the highly interactive approach of UNIPred-Web allow a narrow exploration of specific proteins, and an interactive analysis of large sub-networks with only a few mouse clicks. CONCLUSIONS: UNIPred-Web offers a practical and intuitive (visual) guidance to biologists interested in gaining insights into protein biomolecular functions. UNIPred-Web provides facilities for the integration of networks, and supplies a framework for the imbalance-aware protein network integration of nine organisms, the prediction of thousands of GO protein functions, and a easy-to-use graphical interface for the visual analysis, navigation and interpretation of the integrated networks and of the functional predictions.


Assuntos
Biologia Computacional/métodos , Internet , Mapas de Interação de Proteínas , Proteínas/metabolismo , Software , Algoritmos , Interface Usuário-Computador
12.
Zhonghua Yi Xue Za Zhi ; 99(29): 2311-2314, 2019 Aug 06.
Artigo em Chinês | MEDLINE | ID: mdl-31434409

RESUMO

Objective: To screen the differentially expressed genes, functional enrichment and related signaling pathways in glioma by bioinformatics analysis. Methods: Microarray data of glioma related gene expression profiles were selected in GEO database, and differentially expressed genes in glioma patients and normal brain tissues were screened by R statistical software of lima package. Functional enrichment of differentially expressed genes (GO and KEGG) was performed. The protein-protein interaction database (STRING) was used to analyze the interaction between the screened differentially expressed genes and the related signaling pathways. Results: Two gene expression profiles, GSE15824 and GSE66354, were selected for analysis, and 158 genes with differential expression more than 2 times and P<0.05 were screened. Molecular function (MF) of 158 differentially expressed genes was integrin binding, cell adhesion molecule binding, calcium binding and AMPA glutamate receptor activity. Cell component localization (CC) was located in cell membrane, neuron cell body, axon of nerve cell and so on, while biological process (BP) was mainly cell adhesion and nervous system. Development, cell proliferation, GTPase activity, apoptosis and angiogenesis; KEGG signaling pathways were mainly cAMP signaling pathway, purine metabolism pathway, MAPK signaling pathway and cGMP-PKG signaling pathway. There were 177 interaction connections in 158 differential expression gene-protein interaction networks, with an average interaction of 2.39 between each node and an aggregation coefficient of 0.37. Cytohubb screened the key genes (hub genes) in the signaling pathway. The results indicated that SLC6A1,SLC1A2,BDNF,GAP43,NRXN1,GAD1,OLIG2, PLP1,S100B and GRIA3 were the key genes in the signaling pathway of the interacting protein network. All the 10 key genes were related to the prognosis of patients (P<0.05). Conclusions: There are differentially expressed genes profile in glioma tissues and normal tissues. SLC6A1, SLC1A2, BDNF, GAP43, NRXN1, GAD1, OLIG2, PLP1, S100B and GRIA3 are key genes for glioma development and are related to the prognosis of patients.


Assuntos
Biologia Computacional , Glioma , Proteínas da Membrana Plasmática de Transporte de GABA , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Humanos , Transdução de Sinais
14.
Stud Health Technol Inform ; 264: 1845-1846, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31438372

RESUMO

Next-generation sequencing has prompted the development of numerous -omics applications. Along with experimental procedures, various computational pipelines became available to address the inherent complexities concerning the volume and quality of data. These pipelines are effective and routinely applied; however, interpreting their outcomes into actionable evidence is still poorly addressed. In this context, this work proposes a method for translating patient genomic profiles to drug response aberrations by integrating pharmacogenomic data into sequencing data analysis pipelines.


Assuntos
Biologia Computacional , Exoma , Relação Dose-Resposta a Droga , Genômica , Sequenciamento de Nucleotídeos em Larga Escala , Humanos
15.
Bioengineered ; 10(1): 345-352, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31411110

RESUMO

This study aimed to detect serum miR-203 expression levels in AML and explore its potential clinical significance. Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) was performed to measure the serum miR-203 levels in 134 patients with AML and 70 healthy controls. The results demonstrated that serum miR-203 expression was significantly reduced in AML patients compared with healthy controls. Receiver operating characteristic curve (ROC) analysis revealed miR-203 could distinguish AML cases from normal controls. Low serum miR-203 levels were associated with worse clinical features, as well as poorer overall survival and relapse free survival of AML patients. Moreover, multivariate analysis confirmed low serum miR-203 expression to be an independent unfavorable prognostic predictor for AML. The bioinformatics analysis showed that the downstream genes and pathways of miR-203 was closely associated with tumorigenesis. Downregulation of miR-203 in AML cell lines upregulated the expression levels of oncogenic promoters such as CREB1, SRC and HDAC1. Thus, these findings demonstrated that serum miR-203 might be a promising biomarker for the diagnosis and prognosis of AML.


Assuntos
Biomarcadores Tumorais/genética , Carcinogênese/genética , Regulação Leucêmica da Expressão Gênica , Leucemia Mieloide Aguda/genética , MicroRNAs/genética , Proteínas de Neoplasias/genética , Antagomirs/genética , Antagomirs/metabolismo , Biomarcadores Tumorais/sangue , Carcinogênese/metabolismo , Carcinogênese/patologia , Estudos de Casos e Controles , Linhagem Celular Tumoral , Biologia Computacional/métodos , Proteína de Ligação ao Elemento de Resposta ao AMP Cíclico/sangue , Proteína de Ligação ao Elemento de Resposta ao AMP Cíclico/genética , Perfilação da Expressão Gênica , Ontologia Genética , Histona Desacetilase 1/sangue , Histona Desacetilase 1/genética , Humanos , Leucemia Mieloide Aguda/sangue , Leucemia Mieloide Aguda/mortalidade , Leucemia Mieloide Aguda/patologia , MicroRNAs/antagonistas & inibidores , MicroRNAs/sangue , Anotação de Sequência Molecular , Análise Multivariada , Proteínas de Neoplasias/sangue , Prognóstico , Curva ROC , Recidiva , Transdução de Sinais , Análise de Sobrevida , Quinases da Família src/sangue , Quinases da Família src/genética
16.
Adv Exp Med Biol ; 1158: 143-182, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31452140

RESUMO

Mitochondrial dysfunction is discussed as a key player in the pathogenesis of type 2 diabetes mellitus (T2Dm), a highly prevalent disease rapidly developing as one of the greatest global health challenges of this century. Data however about the involvement of mitochondria, central hubs in bioenergetic processes, in the disease development are still controversial. Lipid and protein homeostasis are under intense discussion to be crucial for proper mitochondrial function. Consequently proteomics and lipidomics analyses might help to understand how molecular changes in mitochondria translate to alterations in energy transduction as observed in the healthy and metabolic diseases such as T2Dm and other related disorders. Mitochondrial lipids integrated in a tool covering proteomic and functional analyses were up to now rarely investigated, although mitochondrial lipids might provide a possible lynchpin in the understanding of type 2 diabetes development and thereby prevention. In this chapter state-of-the-art analytical strategies, pre-analytical aspects, potential pitfalls as well as current proteomics and lipidomics-based knowledge about the pathophysiological role of mitochondria in the pathogenesis of type 2 diabetes will be discussed.


Assuntos
Biologia Computacional , Diabetes Mellitus Tipo 2 , Fígado , Mitocôndrias , Músculo Esquelético , Proteômica , Diabetes Mellitus Tipo 2/fisiopatologia , Humanos , Metabolismo dos Lipídeos , Fígado/fisiopatologia , Mitocôndrias/metabolismo , Músculo Esquelético/fisiopatologia
17.
BMC Bioinformatics ; 20(1): 409, 2019 Jul 30.
Artigo em Inglês | MEDLINE | ID: mdl-31362694

RESUMO

BACKGROUND: Internal ribosome entry sites (IRES) are segments of mRNA found in untranslated regions that can recruit the ribosome and initiate translation independently of the 5' cap-dependent translation initiation mechanism. IRES usually function when 5' cap-dependent translation initiation has been blocked or repressed. They have been widely found to play important roles in viral infections and cellular processes. However, a limited number of confirmed IRES have been reported due to the requirement for highly labor intensive, slow, and low efficiency laboratory experiments. Bioinformatics tools have been developed, but there is no reliable online tool. RESULTS: This paper systematically examines the features that can distinguish IRES from non-IRES sequences. Sequence features such as kmer words, structural features such as QMFE, and sequence/structure hybrid features are evaluated as possible discriminators. They are incorporated into an IRES classifier based on XGBoost. The XGBoost model performs better than previous classifiers, with higher accuracy and much shorter computational time. The number of features in the model has been greatly reduced, compared to previous predictors, by including global kmer and structural features. The contributions of model features are well explained by LIME and SHapley Additive exPlanations. The trained XGBoost model has been implemented as a bioinformatics tool for IRES prediction, IRESpy (https://irespy.shinyapps.io/IRESpy/), which has been applied to scan the human 5' UTR and find novel IRES segments. CONCLUSIONS: IRESpy is a fast, reliable, high-throughput IRES online prediction tool. It provides a publicly available tool for all IRES researchers, and can be used in other genomics applications such as gene annotation and analysis of differential gene expression.


Assuntos
Biologia Computacional/métodos , Sítios Internos de Entrada Ribossomal/genética , Software , Regiões 5' não Traduzidas/genética , Algoritmos , Sequência de Bases , Humanos , Modelos Teóricos , Probabilidade , RNA Viral/genética
18.
BMC Bioinformatics ; 20(1): 410, 2019 Jul 30.
Artigo em Inglês | MEDLINE | ID: mdl-31362714

RESUMO

BACKGROUND: Antiretroviral drugs are a very effective therapy against HIV infection. However, the high mutation rate of HIV permits the emergence of variants that can be resistant to the drug treatment. Predicting drug resistance to previously unobserved variants is therefore very important for an optimum medical treatment. In this paper, we propose the use of weighted categorical kernel functions to predict drug resistance from virus sequence data. These kernel functions are very simple to implement and are able to take into account HIV data particularities, such as allele mixtures, and to weigh the different importance of each protein residue, as it is known that not all positions contribute equally to the resistance. RESULTS: We analyzed 21 drugs of four classes: protease inhibitors (PI), integrase inhibitors (INI), nucleoside reverse transcriptase inhibitors (NRTI) and non-nucleoside reverse transcriptase inhibitors (NNRTI). We compared two categorical kernel functions, Overlap and Jaccard, against two well-known noncategorical kernel functions (Linear and RBF) and Random Forest (RF). Weighted versions of these kernels were also considered, where the weights were obtained from the RF decrease in node impurity. The Jaccard kernel was the best method, either in its weighted or unweighted form, for 20 out of the 21 drugs. CONCLUSIONS: Results show that kernels that take into account both the categorical nature of the data and the presence of mixtures consistently result in the best prediction model. The advantage of including weights depended on the protein targeted by the drug. In the case of reverse transcriptase, weights based in the relative importance of each position clearly increased the prediction performance, while the improvement in the protease was much smaller. This seems to be related to the distribution of weights, as measured by the Gini index. All methods described, together with documentation and examples, are freely available at https://bitbucket.org/elies_ramon/catkern.


Assuntos
Algoritmos , Biologia Computacional/métodos , Farmacorresistência Viral/genética , HIV-1/genética , Fármacos Anti-HIV/farmacologia , Farmacorresistência Viral/efeitos dos fármacos , Infecções por HIV/virologia , HIV-1/efeitos dos fármacos , HIV-1/isolamento & purificação , Humanos , Modelos Lineares , Análise de Componente Principal
19.
Hum Genet ; 138(10): 1171-1182, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31367973

RESUMO

Age-related macular degeneration (AMD) is a leading cause of blindness in the world. While dozens of independent genomic variants are associated with AMD, about one-third of AMD heritability is still unexplained. To identify novel variants and loci for AMD, we analyzed Illumina HumanExome chip data from 87 Amish individuals with early or late AMD, 79 unaffected Amish individuals, and 15 related Amish individuals with unknown AMD affection status. We retained 37,428 polymorphic autosomal variants across 175 samples for association and linkage analyses. After correcting for multiple testing (n = 37,428), we identified four variants significantly associated with AMD: rs200437673 (LCN9, p = 1.50 × 10-11), rs151214675 (RTEL1, p = 3.18 × 10-8), rs140250387 (DLGAP1, p = 4.49 × 10-7), and rs115333865 (CGRRF1, p = 1.05 × 10-6). These variants have not been previously associated with AMD and are not in linkage disequilibrium with the 52 known AMD-associated variants reported by the International AMD Genomics Consortium based on physical distance. Genome-wide significant linkage peaks were observed on chromosomes 8q21.11-q21.13 (maximum recessive HLOD = 4.03) and 18q21.2-21.32 (maximum dominant HLOD = 3.87; maximum recessive HLOD = 4.27). These loci do not overlap with loci previously linked to AMD. Through gene ontology enrichment analysis with ClueGO in Cytoscape, we determined that several genes in the 1-HLOD support interval of the chromosome 8 locus are involved in fatty acid binding and triglyceride catabolic processes, and the 1-HLOD support interval of the linkage region on chromosome 18 is enriched in genes that participate in serine-type endopeptidase inhibitor activity and the positive regulation of epithelial to mesenchymal transition. These results nominate novel variants and loci for AMD that require further investigation.


Assuntos
Amish/genética , Predisposição Genética para Doença , Variação Genética , Degeneração Macular/genética , Locos de Características Quantitativas , Idoso , Idoso de 80 Anos ou mais , Alelos , Biologia Computacional , Feminino , Frequência do Gene , Ontologia Genética , Estudos de Associação Genética , Ligação Genética , Humanos , Indiana , Masculino , Ohio , Linhagem
20.
Gene ; 720: 144035, 2019 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-31404595

RESUMO

Alcoholic hepatitis (AH) is a severe form of alcoholic liver disease associated with high mortality. Current pharmacological treatment options are not fully effective, and novel target therapies are urgently needed. Until now, key genes, miRNAs and potential signaling pathways in AH remain unclear. Here, we integrated mRNA and miRNA expression profiles to reveal 1411 differentially expressed genes (DEG) and 69 differentially expressed miRNAs (DEM) in AH. And then 51 overlapping genes were identified by compared with miRNA target genes and DEGs, which named as consistent expression genes (CEGs). Pathway analysis showed that CEGs were mainly enriched in PI3K-Akt signaling pathway, MicroRNAs in cancer, FoxO signaling pathway, TNF signaling pathway and P53 signaling pathway. A total of 8 hub genes,FOS, FOXO1, SIRT1, ESR1, BCL2L11, CDK1, CCNB1 and CDKN1A, were screened using protein-protein interaction network analysis. In the regulatory network of miRNA and hub genes, a total of five miRNAs, miR-29c, miR-92b, miR-132, miR-221, miR-222, were identified as key miRNAs. Among them, miR-132 has been shown to target SIRT1, FOXO1, CDKN1A and BCL2L11, and miR-92b targets SIRT1 and BCL2L11. miR-221 and miR-222 both target FOS, ESR1, and BCL2L11. In addition, miR-29c is one of the major down-regulated miRNAs in AH, targeting FOS. Western blot analysis showed that SIRT1 and FoxO1 were expressed at low levels (P < 0.05) and CDK1 was highly expressed in the AH group (P < 0.05). The other five proteins were not significantly different between the two groups (P > 0.05). RT-PCR results showed that miR-132 was significantly higher in the AH group than in the normal group (P < 0.05), while miR-29c was lower than the normal group (P < 0.05), and the other three miRNAs were not significantly different between the two groups (P > 0.05). Therefore, SIRT1, FOXO1, CDK1, miR-132 and miR-29c are involved in the regulation of FoxO and P53 signaling pathways, cell cycle and other biological processes, which may play a key role in the pathogenesis of AH.


Assuntos
Biomarcadores/análise , Biologia Computacional/métodos , Regulação da Expressão Gênica , Redes Reguladoras de Genes , Hepatite Alcoólica/genética , MicroRNAs/genética , Transdução de Sinais , Estudos de Casos e Controles , Feminino , Perfilação da Expressão Gênica , Humanos , Masculino , Pessoa de Meia-Idade
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