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1.
Braz. j. biol ; 83: e243910, 2023. tab, graf
Artigo em Inglês | LILACS, VETINDEX | ID: biblio-1278525

RESUMO

Abstract Nucleotide excision repair (NER) acts repairing damages in DNA, such as lesions caused by cisplatin. Xeroderma Pigmentosum complementation group C (XPC) protein is involved in recognition of global genome DNA damages during NER (GG-NER) and it has been studied in different organisms due to its importance in other cellular processes. In this work, we studied NER proteins in Trypanosoma cruzi and Trypanosoma evansi, parasites of humans and animals respectively. We performed three-dimensional models of XPC proteins from T. cruzi and T. evansi and observed few structural differences between these proteins. In our tests, insertion of XPC gene from T. evansi (TevXPC) in T. cruzi resulted in slower cell growth under normal conditions. After cisplatin treatment, T. cruzi overexpressing its own XPC gene (TcXPC) was able to recover cell division rates faster than T. cruzi expressing TevXPC gene. Based on these tests, it is suggested that TevXPC (being an exogenous protein in T. cruzi) interferes negatively in cellular processes where TcXPC (the endogenous protein) is involved. This probably occurred due interaction of TevXPC with some endogenous molecules or proteins from T.cruzi but incapacity of interaction with others. This reinforces the importance of correctly XPC functioning within the cell.


Resumo O reparo por excisão de nucleotídeos (NER) atua reparando danos no DNA, como lesões causadas por cisplatina. A proteína Xeroderma Pigmentosum complementation group C (XPC) está envolvida no reconhecimento de danos pela via de reparação global do genoma pelo NER (GG-NER) e tem sido estudada em diferentes organismos devido à sua importância em outros processos celulares. Neste trabalho, estudamos proteínas do NER em Trypanosoma cruzi e Trypanosoma evansi, parasitos de humanos e animais, respectivamente. Modelos tridimensionais das proteínas XPC de T. cruzi e T. evansi foram feitos e observou-se poucas diferenças estruturais entre estas proteínas. Durante testes, a inserção do gene XPC de T. evansi (TevXPC) em T. cruzi resultou em crescimento celular mais lento em condições normais. Após o tratamento com cisplatina, T. cruzi superexpressando seu próprio gene XPC (TcXPC) foi capaz de recuperar as taxas de divisão celular mais rapidamente do que T. cruzi expressando o gene TevXPC. Com base nesses testes, sugere-se que TevXPC (sendo uma proteína exógena em T. cruzi) interfere negativamente nos processos celulares em que TcXPC (a proteína endógena) está envolvida. Isso provavelmente ocorreu pois TevXPC é capaz de interagir com algumas moléculas ou proteínas endógenas de T.cruzi, mas é incapaz de interagir com outras. Isso reforça a importância do correto funcionamento de XPC dentro da célula.


Assuntos
Humanos , Animais , Trypanosoma cruzi/genética , Xeroderma Pigmentoso , Dano ao DNA/genética , Biologia Computacional , Proteínas de Ligação a DNA/genética , Proteínas de Ligação a DNA/metabolismo , Reparo do DNA/genética
2.
J Mol Neurosci ; 72(3): 468-481, 2022 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-34580818

RESUMO

Neuropathic pain (NP) involves metabolic processes that are regulated by metabolic genes and their non-coding regulator genes such as microRNAs (miRNAs). Here, we aimed at exploring the key miRNA signatures regulating metabolic genes involved in NP pathogenesis. We downloaded NP-related data from public databases and identified differentially expressed microRNAs (miRNAs) and mRNAs through differential gene expression analysis. The miRNA target prediction was performed, and integration with the differentially expressed metabolic genes (DEMGs) was used for constructing the miRNA-DEMG network. Subsequently, functional enrichment analysis and protein-protein interaction (PPI) analysis were performed to explore the role of DEMGs in the regulatory network. The drug prediction was performed based on the DEMGs in the miRNA-DEMG network. A total of 8251 differentially expressed mRNAs (4193 upregulated and 4058 downregulated), and 959 differentially expressed miRNAs (455 upregulated and 504 downregulated) were identified. Moreover, after target gene prediction, a miRNA-DEMG network composed of 22 miRNAs and 113 mRNAs was constructed. The network was constituted of 135 nodes and 236 edges. We found that DEMGs in the network were mainly enriched in metabolic pathways and metabolic processes. A total of 1200 drugs were predicted as potential therapeutics for NP based on the differentially expressed genes, while 170 drugs were predicted for the DEMGs in the miRNA-DEMG network. Conclusively, our study predicted drugs that may be effective against the metabolic changes induced by miRNA dysregulation in NP. This information will help further reveal the pathological mechanism of NP and provide more treatment options for NP patients.


Assuntos
MicroRNAs , Neuralgia , Biologia Computacional , Redes Reguladoras de Genes , Humanos , MicroRNAs/genética , MicroRNAs/metabolismo , Neuralgia/tratamento farmacológico , Neuralgia/genética , RNA Mensageiro/genética , RNA Mensageiro/metabolismo
3.
Front Immunol ; 13: 843128, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35928817

RESUMO

Bidirectional cross-talk between commensal microbiota and the immune system is essential for the regulation of immune responses and the formation of immunological memory. Perturbations of microbiome-immune system interactions can lead to dysregulated immune responses against invading pathogens and/or to the loss of self-tolerance, leading to systemic inflammation and genesis of several immune-mediated pathologies, including neurodegeneration. In this paper, we first investigated the contribution of the immunomodulatory effects of microbiota (bacteria and fungi) in shaping immune responses and influencing the formation of immunological memory cells using a network-based bioinformatics approach. In addition, we investigated the possible role of microbiota-host-immune system interactions and of microbiota-virus interactions in a group of neurodegenerative diseases (NDs): Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), Parkinson's disease (PD) and Alzheimer's disease (AD). Our analysis highlighted various aspects of the innate and adaptive immune response systems that can be modulated by microbiota, including the activation and maturation of microglia which are implicated in the development of NDs. It also led to the identification of specific microbiota components which might be able to influence immune system processes (ISPs) involved in the pathogenesis of NDs. In addition, it indicated that the impact of microbiota-derived metabolites in influencing disease-associated ISPs, is higher in MS disease, than in AD, PD and ALS suggesting a more important role of microbiota mediated-immune effects in MS.


Assuntos
Esclerose Amiotrófica Lateral , Microbiota , Doenças Neurodegenerativas , Doença de Parkinson , Viroses , Biologia Computacional , Humanos , Imunidade
4.
BMC Bioinformatics ; 23(1): 323, 2022 Aug 06.
Artigo em Inglês | MEDLINE | ID: mdl-35933367

RESUMO

BACKGROUND: A key problem in bioinformatics is that of predicting gene expression levels. There are two broad approaches: use of mechanistic models that aim to directly simulate the underlying biology, and use of machine learning (ML) to empirically predict expression levels from descriptors of the experiments. There are advantages and disadvantages to both approaches: mechanistic models more directly reflect the underlying biological causation, but do not directly utilize the available empirical data; while ML methods do not fully utilize existing biological knowledge. RESULTS: Here, we investigate overcoming these disadvantages by integrating mechanistic cell signalling models with ML. Our approach to integration is to augment ML with similarity features (attributes) computed from cell signalling models. Seven sets of different similarity feature were generated using graph theory. Each set of features was in turn used to learn multi-target regression models. All the features have significantly improved accuracy over the baseline model - without the similarity features. Finally, the seven multi-target regression models were stacked together to form an overall prediction model that was significantly better than the baseline on 95% of genes on an independent test set. The similarity features enable this stacking model to provide interpretable knowledge about cancer, e.g. the role of ERBB3 in the MCF7 breast cancer cell line. CONCLUSION: Integrating mechanistic models as graphs helps to both improve the predictive results of machine learning models, and to provide biological knowledge about genes that can help in building state-of-the-art mechanistic models.


Assuntos
Aprendizado de Máquina , Neoplasias , Biologia Computacional/métodos , Expressão Gênica , Humanos
5.
Cell Death Dis ; 13(8): 686, 2022 Aug 06.
Artigo em Inglês | MEDLINE | ID: mdl-35933468

RESUMO

Acute lung injury (ALI) is a potentially life-threatening, devastating disease with an extremely high rate of mortality. The underlying mechanism of ALI is currently unclear. In this study, we aimed to confirm the hub genes associated with ALI and explore their functions and molecular mechanisms using bioinformatics methods. Five microarray datasets available in GEO were used to perform Robust Rank Aggregation (RRA) to identify differentially expressed genes (DEGs) and the key genes were identified via the protein-protein interaction (PPI) network. Lipopolysaccharide intraperitoneal injection was administered to establish an ALI model. Overall, 40 robust DEGs, which are mainly involved in the inflammatory response, protein catabolic process, and NF-κB signaling pathway were identified. Among these DEGs, we identified two genes associated with ALI, of which the CAV-1/NF-κB axis was significantly upregulated in ALI, and was identified as one of the most effective targets for ALI prevention. Subsequently, the expression of CAV-1 was knocked down using AAV-shCAV-1 or CAV-1-siRNA to study its effect on the pathogenesis of ALI in vivo and in vitro. The results of this study indicated that CAV-1/NF-κB axis levels were elevated in vivo and in vitro, accompanied by an increase in lung inflammation and autophagy. The knockdown of CAV-1 may improve ALI. Mechanistically, inflammation was reduced mainly by decreasing the expression levels of CD3 and F4/80, and activating autophagy by inhibiting AKT/mTOR and promoting the AMPK signaling pathway. Taken together, this study provides crucial evidence that CAV-1 knockdown inhibits the occurrence of ALI, suggesting that the CAV-1/NF-κB axis may be a promising therapeutic target for ALI treatment.


Assuntos
Lesão Pulmonar Aguda , NF-kappa B , Lesão Pulmonar Aguda/genética , Lesão Pulmonar Aguda/metabolismo , Caveolina 1/genética , Caveolina 1/metabolismo , Biologia Computacional , Humanos , Lipopolissacarídeos/farmacologia , NF-kappa B/genética , NF-kappa B/metabolismo
6.
J Egypt Natl Canc Inst ; 34(1): 33, 2022 Aug 08.
Artigo em Inglês | MEDLINE | ID: mdl-35934727

RESUMO

While majority of the current treatment approaches for cancer remain expensive and are associated with several side effects, development of new treatment modalities takes a significant period of research, time, and expenditure. An alternative novel approach is drug repurposing that focuses on finding new applications for the previously clinically approved drugs. The process of drug repurposing has also been facilitated by current advances in the field of proteomics, genomics, and information computational biology. This approach not only provides cheaper, effective, and potentially safer drugs with less side effects but also increases the processing pace of drug development. In this review, we wish to highlight some recent developments in the area of drug repurposing in cancer with a specific focus on the repurposing potential of anti-psychotic, anti-inflammatory and anti-viral drugs, anti-diabetic, antibacterial, and anti-fungal drugs.


Assuntos
Reposicionamento de Medicamentos , Neoplasias , Antibacterianos/uso terapêutico , Biologia Computacional , Humanos , Neoplasias/tratamento farmacológico
7.
BMC Bioinformatics ; 23(1): 328, 2022 Aug 08.
Artigo em Inglês | MEDLINE | ID: mdl-35941549

RESUMO

BACKGROUND: Single-cell RNA-sequencing is revolutionising the study of cellular and tissue-wide heterogeneity in a large number of biological scenarios, from highly tissue-specific studies of disease to human-wide cell atlases. A central task in single-cell RNA-sequencing analysis design is the calculation of cell type-specific genes in order to study the differential impact of different replicates (e.g. tumour vs. non-tumour environment) on the regulation of those genes and their associated networks. The crucial task is the efficient and reliable calculation of such cell type-specific 'marker' genes. These optimise the ability of the experiment to isolate highly-specific cell phenotypes of interest to the analyser. However, while methods exist that can calculate marker genes from single-cell RNA-sequencing, no such method places emphasise on specific cell phenotypes for downstream study in e.g. differential gene expression or other experimental protocols (spatial transcriptomics protocols for example). Here we present SMaSH, a general computational framework for extracting key marker genes from single-cell RNA-sequencing data which reliably characterise highly-specific and niche populations of cells in numerous different biological data-sets. RESULTS: SMaSH extracts robust and biologically well-motivated marker genes, which characterise a given single-cell RNA-sequencing data-set better than existing computational approaches for general marker gene calculation. We demonstrate the utility of SMaSH through its substantial performance improvement over several existing methods in the field. Furthermore, we evaluate the SMaSH markers on spatial transcriptomics data, demonstrating they identify highly localised compartments of the mouse cortex. CONCLUSION: SMaSH is a new methodology for calculating robust markers genes from large single-cell RNA-sequencing data-sets, and has implications for e.g. effective gene identification for probe design in downstream analyses spatial transcriptomics experiments. SMaSH has been fully-integrated with the ScanPy framework and provides a valuable bioinformatics tool for cell type characterisation and validation in every-growing data-sets spanning over 50 different cell types across hundreds of thousands of cells.


Assuntos
Biologia Computacional , Transcriptoma , Animais , Biomarcadores , Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , Humanos , Camundongos , RNA , Análise de Sequência de RNA , Análise de Célula Única/métodos
8.
Biomed Res Int ; 2022: 4436646, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35937402

RESUMO

Background: To conduct a comprehensive bioinformatics analysis on the transcriptome signatures of Toll-like receptors (TLRs) in pan-cancer. Materials and methods. A total of 11,057 tissues consisting of 33 types of carcinoma in The Cancer Genome Atlas (TCGA) were retrieved, and then we further explored the correlation between TLRs' expression with tumorigenesis, immune infiltration, and drug sensitivity. We conducted a comprehensive bioinformatics analysis on TLR1 to 10 in pan-cancer, including differential expression analysis between normal and tumor tissues, differential immune subtype correlation, survival analysis, tumor immune infiltration estimating, stemness indices correlation, and drug responses correlation. Results: TLR2 was highly expressed in most types of tumors. TLR9 was hardly expressed compared to other TLR genes, which lead to TLR9 showing less correlation with both immune-estimate scores and stromal-estimate scores. All the TLRs were related with immune subtype of tumor samples that all of them were differentially expressed in differential immune subtype samples. The expression of TLRs was positively related with immune-estimate scores and stromal-estimate scores in almost all types of tumor. The expression of TLRs was negatively correlated with mRNA expression-based stemness scores (RNAss) in nearly almost type of tumors except kidney renal clear cell carcinoma (KIRC) and also negatively correlated with DNA methylation-based stemness scores (DNAss) in many types of tumors except adrenocortical carcinoma (ACC), cholangiocarcinoma (CHOL), KIRC, acute myeloid leukemia (LAML), low-grade glioma (LGG), testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), thymoma (THYM), and uveal melanoma (UVM). The expression of TLR9 was significantly positively correlated with the drug sensitivity of fluphenazine, alectinib, carmustine, and 7-hydroxystaurosporine. TLR7 was significantly positively correlated with the drug sensitivity of alectinib. Conclusions: Our study reveals the significant role of TLRs family in pan-cancer and provides potential therapeutic strategies of cancer.


Assuntos
Carcinoma , Leucemia Mieloide Aguda , Biologia Computacional , Humanos , Receptor Toll-Like 9/genética , Receptores Toll-Like/genética , Receptores Toll-Like/metabolismo
9.
Front Endocrinol (Lausanne) ; 13: 950030, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35937806

RESUMO

Background: Osteoporosis and atherosclerosis are common in the elderly population, conferring a heavy worldwide burden. Evidence links osteoporosis and atherosclerosis but the exact underlying common mechanism of its occurrence is unclear. The purpose of this study is to further explore the molecular mechanism between osteoporosis and atherosclerosis through integrated bioinformatic analysis. Methods: The microarray data of osteoporosis and atherosclerosis in the Gene Expression Omnibus (GEO) database were downloaded. The Weighted Gene Co-Expression Network Analysis (WGCNA) and differentially expressed genes (DEGs) analysis were used to identify the co-expression genes related to osteoporosis and atherosclerosis. In addition, the common gene targets of osteoporosis and atherosclerosis were analyzed and screened through three public databases (CTD, DISEASES, and GeneCards). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Metascape. Then, the common microRNAs (miRNAs) in osteoporosis and atherosclerosis were screened out from the Human microRNA Disease Database (HMDD) and the target genes of whom were predicted through the miRTarbase. Finally, the common miRNAs-genes network was constructed by Cytoscape software. Results: The results of common genes analysis showed that immune and inflammatory response may be a common feature in the pathophysiology of osteoporosis and atherosclerosis. Six hub genes (namely, COL1A1, IBSP, CTSD, RAC2, MAF, and THBS1) were obtained via taking interaction of different analysis results. The miRNAs-genes network showed that has-let-7g might play an important role in the common mechanisms between osteoporosis and atherosclerosis. Conclusion: This study provides new sights into shared molecular mechanisms between osteoporosis and atherosclerosis. These common pathways and hub genes may offer promising clues for further experimental studies.


Assuntos
Aterosclerose , MicroRNAs , Osteoporose , Idoso , Aterosclerose/genética , Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , Regulação Neoplásica da Expressão Gênica , Humanos , MicroRNAs/genética , Osteoporose/genética
10.
Front Endocrinol (Lausanne) ; 13: 934022, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35909518

RESUMO

Diabetic nephropathy (DN) is one of the common chronic complications of diabetes with unclear molecular mechanisms, which is associated with end-stage renal disease (ESRD) and chronic kidney disease (CKD). Our study intended to construct a competing endogenous RNA (ceRNA) network via bioinformatics analysis to determine the potential molecular mechanisms of DN pathogenesis. The microarray datasets (GSE30122 and GSE30529) were downloaded from the Gene Expression Omnibus database to find differentially expressed genes (DEGs). GSE51674 and GSE155188 datasets were used to identified the differentially expressed microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), respectively. The DEGs between normal and DN renal tissues were performed using the Linear Models for Microarray (limma) package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to reveal the mechanisms of DEGs in the progression of DN. The protein-protein interactions (PPI) of DEGs were carried out by STRING database. The lncRNA-miRNA-messenger RNA (mRNA) ceRNA network was constructed and visualized via Cytoscape on the basis of the interaction generated through the miRDB and TargetScan databases. A total of 94 significantly upregulated and 14 downregulated mRNAs, 31 upregulated and 121 downregulated miRNAs, and nine upregulated and 81 downregulated lncRNAs were identified. GO and KEGG pathways enriched in several functions and expression pathways, such as inflammatory response, immune response, identical protein binding, nuclear factor kappa b (NF-κB) signaling pathway, and PI3K-Akt signaling pathway. Based on the analysis of the ceRNA network, five differentially expressed lncRNAs (DElncRNAs) (SNHG6, KCNMB2-AS1, LINC00520, DANCR, and PCAT6), five DEmiRNAs (miR-130b-5p, miR-326, miR-374a-3p, miR-577, and miR-944), and five DEmRNAs (PTPRC, CD53, IRF8, IL10RA, and LAPTM5) were demonstrated to be related to the pathogenesis of DN. The hub genes were validated by using receiver operating characteristic curve (ROC) and real-time PCR (RT-PCR). Our research identified hub genes related to the potential mechanism of DN and provided new lncRNA-miRNA-mRNA ceRNA network that contributed to diagnostic and potential therapeutic targets for DN.


Assuntos
Diabetes Mellitus , Nefropatias Diabéticas , MicroRNAs , RNA Longo não Codificante , Biomarcadores , Biologia Computacional , Nefropatias Diabéticas/genética , Redes Reguladoras de Genes , Humanos , MicroRNAs/genética , Fosfatidilinositol 3-Quinases/genética , RNA Longo não Codificante/genética , RNA Mensageiro/genética
11.
Drug Des Devel Ther ; 16: 2365-2382, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35910781

RESUMO

Background: As the main component of turmeric (Curcuma longa L.), curcumin is widely used in the treatment of various diseases. Previous studies have demonstrated that curcumin has great potential as a therapeutic agent, but the lack of understanding of the functional mechanism of the drug has hindered the widespread use of the natural product. In the present study, we used comprehensive bioinformatics analysis and in vitro experiments to explore the anti-tumor mechanism of curcumin. Materials and Methods: LUAD mRNA expression data were obtained from TCGA database and differentially expressed genes (DEGs) were identified using R software. Functional enrichment analysis was conducted to further clarify its biological properties and hub genes were identified by a protein-protein interaction (PPI) network analysis. Survival analysis and molecular docking were used to analyze the effectiveness of the hub genes. By an in vitro study, we evaluated whether curcumin could influence the proliferation, migration, and invasion activities of LUAD cells. Results: In this study, 1783 DEGs from LUAD tissue samples compared to normal samples were evaluated. Functional enrichment analysis and the PPI network revealed the characteristics of the DEGs. We performed a topological analysis and identified 10 hub genes. Of these, six genes (INS, GCG, SST, F2, AHSG, and NPY) were identified as potentially effective biomarkers of LUAD. The molecular docking results indicated that curcumin targets in regulating lung cancer may be INS and GCG. We found that curcumin significantly inhibited the proliferation, migration, and invasion of LUAD cells and significantly decreased the expression of the INS and GCG genes. Conclusion: The results of this study suggest that the therapeutic effects of curcumin on LUAD may be achieved through the intervention of INS and GCG, which may act as potential biomarkers for LUAD prevention and treatment.


Assuntos
Adenocarcinoma de Pulmão , Curcumina , Neoplasias Pulmonares , Adenocarcinoma de Pulmão/tratamento farmacológico , Adenocarcinoma de Pulmão/genética , Biomarcadores Tumorais , Biologia Computacional , Curcumina/farmacologia , Regulação Neoplásica da Expressão Gênica , Humanos , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/genética , Simulação de Acoplamento Molecular
12.
Biomed Eng Online ; 21(1): 51, 2022 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-35915455

RESUMO

BACKGROUND: Vulvar lichen sclerosus (VLS) is one of the most common clinical manifestations of vulva. Thirteen percent of women have symptomatic vulvar diseases. The aim of this study is to investigate the expression profile of circular RNA (circRNAs) in vulvar lichen sclerosus, and to identify the underlying core genes of VLS. METHODS: We removed rRNA for sequencing, and screened the differentially expressed messenger RNA (mRNAs), long non-coding RNA (lncRNAs) and single-stranded circRNA in 20 groups of VLS tissues and 20 groups of healthy female vulvar skin tissues. Bioinformatics analysis was used to analyze its potential functions. RESULTS: A total of 2545 differentially expressed mRNAs were assessed in VLS patients, of which 1541 samples were up-regulated and 1004 samples were down-regulated. A total of 1453 differentially expressed lncRNAs were assessed, of which 812 samples were up-regulated and 641 samples were down-regulated. A total of 79 differentially expressed circRNAs were assessed, of which 54 were up-regulated and 25 were down-regulated. The differential expression of circRNAs was closely related to biological processes and molecular functions. The differences in circRNAs were mainly related to the "human T-cell leukemia virus 1 infection" signaling pathway and the "axon guidance" signaling pathway. CONCLUSION: The profile of abnormal regulation of circRNA exists in VLS. According to biological informatics analysis, the dysregulation of circRNAs may be related to the pathogenesis and pathological process of VLS.


Assuntos
RNA Longo não Codificante , Líquen Escleroso Vulvar , Biologia Computacional , Feminino , Humanos , RNA Circular/genética , RNA Longo não Codificante/genética , RNA Mensageiro/genética , RNA Mensageiro/metabolismo
13.
Biomed Res Int ; 2022: 8080679, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35915795

RESUMO

Objective: To investigate the main pharmacological basis and mechanism of action of Gujiansan in the treatment of steroid-induced avascular necrosis of the femoral head (SANFH). Methods: The active constituents and targets of Gujiansan were screened by using TCMSP and other databases, and relevant disease targets were obtained by analyzing the microarray of SANFH in the GEO database. The intersection of the two was taken to obtain the potential targets of Gujiansan for the treatment of SANFH, and key active constituents were screened with the "active constituent-target" network constructed by the Cytoscape software; then, the STRING database was used to construct the protein interaction network to screen the key targets. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of key targets were performed by the DAVID database, and the relationship between the "key active constituent-key target-key signaling pathway" was explored. Finally, the molecular docking between key active constituents and key targets was verified. In addition, qPCR detection technology was used to evaluate the preventive and therapeutic effects of key active constituents of Gujiansan in a rat osteoblast model of SANFH to verify the possible mechanism of the effect of Gujiansan in the treatment of SANFH. Results: (1) 106 active constituents and 55 targets were obtained for the treatment of SANFH. (2) Quercetin, luteolin, kaempferol, cryptotanshinone, and naringenin were the key active constituents for the treatment of SANFH. (3) IL1B, STAT3, CAT, PTGS2, and MAPK3 were the key targets for the treatment of SANFH. (4) IL1B, STAT3, CAT, PTGS2, MAPK3, and HMOX1 are key targets in the protein interaction network. (5) DAVID enrichment analysis mainly covers the regulation of DNA-binding transcription factor activity, positive regulation of cytokine production, and response to oxidative stress and other biological processes, involving IL-17, AGE-RAGE, C-type lectin receptor, and other signaling pathways. (6) Gujiansan is a multitarget and multisignaling pathway for the treatment of SANFH. (7) Good binding activity exists between key active constituents and key targets. Conclusion: This study analyzes the potential mechanism of action of Gujiansan in the treatment of SANFH with network pharmacology, which can provide a reference for the further study of its pharmacological basis and targets.


Assuntos
Medicamentos de Ervas Chinesas , Necrose da Cabeça do Fêmur , Animais , Biologia Computacional , Ciclo-Oxigenase 2 , Medicamentos de Ervas Chinesas/química , Necrose da Cabeça do Fêmur/induzido quimicamente , Necrose da Cabeça do Fêmur/tratamento farmacológico , Necrose da Cabeça do Fêmur/genética , Medicina Tradicional Chinesa , Simulação de Acoplamento Molecular , Ratos , Esteroides
14.
Genet Res (Camb) ; 2022: 1415140, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35919038

RESUMO

Background: There is still no clear understanding of the pathogenesis of atrial fibrillation (AF). For this purpose, we used integrated analysis to uncover immune infiltration characteristics and investigated their relationship with competing endogenous RNA (ceRNA) network in AF. Methods: Three AF mRNA data sets (GSE14975, GSE79768, and GSE41177) were integrated using the SVA method from Gene Expression Omnibus (GEO). Together with AF circRNA data set (GSE129409) and miRNA data set (GSE70887) from GEO database, we built a ceRNA network. Then hub genes were screened by the Cytoscape plug-in cytoHubba from a protein-protein interaction (PPI) network. As well, CIBERSORT was employed to investigate immune infiltration, followed by Pearson correlation coefficients to unravel the correlation between AF-related infiltrating immune cells and hub genes. Ulteriorly, circRNA-miRNA-mRNA regulatory axises that could be immunologically related to AF were obtained. Results: Ten hub genes were identified from the constructing PPI network. The immune infiltration analysis revealed that the number of monocytes and neutrophils was higher, as well as the number of dendritic cells activated and T cells regulatory (Tregs) was lower in AF. Seven hub genes (C5AR1, CXCR4, HCK, LAPTM5, MPEG1, TLR8, and TNFSF13B) were associated with those 4 immune cells (P < 0.05). We found that the circ_0005299-miR-1246-C5AR1 and circRNA_0079284-miR-623-HCK/CXCR4 regulatory axises may be associated with the immune mechanism of AF. Conclusion: The findings of our study provide insights into immuno-related ceRNA networks as potential molecular regulators of AF progression.


Assuntos
Fibrilação Atrial , MicroRNAs , Fibrilação Atrial/genética , Biologia Computacional/métodos , Redes Reguladoras de Genes/genética , Humanos , MicroRNAs/genética , MicroRNAs/metabolismo , RNA Circular/genética , RNA Mensageiro/genética , RNA Mensageiro/metabolismo
15.
Methods Mol Biol ; 2516: 61-79, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35922622

RESUMO

Gene regulation is an intricate phenomenon involving precise function of many macromolecular complexes. Molecular basis of this phenomenon is highly complex and cannot be fully understood using a single technique. Computational approaches can play a crucial role in overall understanding of functional and mechanistic features of a protein or an assembly. Large amounts of structural data pertaining to these complexes are publicly available. In this project, we took advantage of the availability of the structural information to unravel functional intricacies of Mycobacterium tuberculosis RNA polymerase upon interaction with RbpA. In this article, we discuss how the knowledge on protein structure and dynamics can be exploited to study function using various computational tools and resources. Overall, this article provides an overview of various computational methods which can be efficiently used to understand the role of any protein. We hope especially the nonexperts in the field could benefit from our article.


Assuntos
Mycobacterium tuberculosis , Proteínas de Bactérias/metabolismo , Biologia Computacional , RNA Polimerases Dirigidas por DNA/metabolismo , Mycobacterium tuberculosis/genética , Mycobacterium tuberculosis/metabolismo , Regiões Promotoras Genéticas
16.
Front Endocrinol (Lausanne) ; 13: 864407, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35923621

RESUMO

Background: This study aimed to identify biological markers for diabetic nephropathy (DN) and explore their underlying mechanisms. Methods: Four datasets, GSE30528, GSE47183, GSE104948, and GSE96804, were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the "limma" package, and the "RobustRankAggreg" package was used to screen the overlapping DEGs. The hub genes were identified using cytoHubba of Cytoscape. Logistic regression analysis was used to further analyse the hub genes, followed by receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. Correlation analysis and enrichment analysis of the hub genes were performed to identify the potential functions of the hub genes involved in DN. Results: In total, 55 DEGs, including 38 upregulated and 17 downregulated genes, were identified from the three datasets. Four hub genes (FN1, CD44, C1QB, and C1QA) were screened out by the "UpSetR" package, and FN1 was identified as a key gene for DN by logistic regression analysis. Correlation analysis and enrichment analysis showed that FN1 was positively correlated with four genes (COL6A3, COL1A2, THBS2, and CD44) and with the development of DN through the extracellular matrix (ECM)-receptor interaction pathway. Conclusions: We identified four candidate genes: FN1, C1QA, C1QB, and CD44. On further investigating the biological functions of FN1, we showed that FN1 was positively correlated with THBS2, COL1A2, COL6A3, and CD44 and involved in the development of DN through the ECM-receptor interaction pathway. THBS2, COL1A2, COL6A3, and CD44 may be novel biomarkers and target therapeutic candidates for DN.


Assuntos
Diabetes Mellitus , Nefropatias Diabéticas , Biomarcadores , Biologia Computacional , Nefropatias Diabéticas/genética , Perfilação da Expressão Gênica , Humanos , Transdução de Sinais/genética
17.
Comput Math Methods Med ; 2022: 4490154, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35924115

RESUMO

MicroRNAs (miRNAs) are a kind of noncoding RNA, which plays an essential role in gene regulation by binding to messenger RNAs (mRNAs). Accurate and rapid identification of miRNA target genes is helpful to reveal the mechanism of transcriptome regulation, which is of great significance for the study of cancer and other diseases. Many bioinformatics methods have been proposed to solve this problem, but the previous research did not further study the encoding of the nucleotide sequence. In this paper, we developed a novel method combining word embedding and deep learning for human miRNA targets at the site-level prediction, which is inspired by the similarity between natural language and biological sequences. First, the word2vec model was used to mine the distribution representation of miRNAs and mRNAs. Then, the embedding is extracted automatically via the stacked bidirectional long short-term memory (BiLSTM) network. By testing, our method can effectively improve the accuracy, sensitivity, specificity, and F-measure of other methods. Through our research, it is proved that the distributed representation can improve the accuracy of the deep learning model and better solve the miRNA target site prediction problem.


Assuntos
Aprendizado Profundo , MicroRNAs , Biologia Computacional/métodos , Humanos , MicroRNAs/genética , MicroRNAs/metabolismo , RNA Mensageiro/genética , Transcriptoma
18.
Structure ; 30(8): 1047-1049, 2022 Aug 04.
Artigo em Inglês | MEDLINE | ID: mdl-35931059

RESUMO

Accurate protein structure predictors use clusters of homologues, which disregard sequence specific effects. In this issue of Structure, Weißenow and colleagues report a deep learning-based tool, EMBER2, that efficiently predicts the distances in a protein structure from its amino acid sequence only. This approach should enable the analysis of mutation effects.


Assuntos
Biologia Computacional , Aprendizado Profundo , Sequência de Aminoácidos , Idioma , Proteínas/química
19.
BMC Bioinformatics ; 23(1): 319, 2022 Aug 05.
Artigo em Inglês | MEDLINE | ID: mdl-35931960

RESUMO

BACKGROUND: Visceral Leishmaniasis (VL) is a fatal vector-borne parasitic disorder occurring mainly in tropical and subtropical regions. VL falls under the category of neglected tropical diseases with growing drug resistance and lacking a licensed vaccine. Conventional vaccine synthesis techniques are often very laborious and challenging. With the advancement of bioinformatics and its application in immunology, it is now more convenient to design multi-epitope vaccines comprising predicted immuno-dominant epitopes of multiple antigenic proteins. We have chosen four antigenic proteins of Leishmania donovani and identified their T-cell and B-cell epitopes, utilizing those for in-silico chimeric vaccine designing. The various physicochemical characteristics of the vaccine have been explored and the tertiary structure of the chimeric construct is predicted to perform docking studies and molecular dynamics simulations. RESULTS: The vaccine construct is generated by joining the epitopes with specific linkers. The predicted tertiary structure of the vaccine has been found to be valid and docking studies reveal the construct shows a high affinity towards the TLR-4 receptor. Population coverage analysis shows the vaccine can be effective on the majority of the world population. In-silico immune simulation studies confirms the vaccine to raise a pro-inflammatory response with the proliferation of activated T and B cells. In-silico codon optimization and cloning of the vaccine nucleic acid sequence have also been achieved in the pET28a vector. CONCLUSION: The above bioinformatics data support that the construct may act as a potential vaccine. Further wet lab synthesis of the vaccine and in vivo works has to be undertaken in animal model to confirm vaccine potency.


Assuntos
Leishmania donovani , Leishmaniose Visceral , Biologia Computacional/métodos , Epitopos de Linfócito B , Epitopos de Linfócito T/química , Humanos , Leishmaniose Visceral/prevenção & controle , Simulação de Acoplamento Molecular , Vacinas de Subunidades/química
20.
World J Surg Oncol ; 20(1): 252, 2022 Aug 05.
Artigo em Inglês | MEDLINE | ID: mdl-35932027

RESUMO

BACKGROUND: Hepatocellular carcinoma (HCC) as a common tumor has a poor prognosis. Recently, a combination of atezolizumab and bevacizumab has been recommended as the preferred regimen for advanced HCC. However, the overall response rate of this therapy is low. There is an urgent need to identify sensitive individuals for this precise therapy among HCC patients. METHODS: The Wilcox test was used to screen the differentially expressed immune-related genes by combining the TCGA cohort and the Immunology Database. Univariate and multivariate Cox regression analysis were used to screen the immune gene pairs concerning prognosis. A predictive model was constructed using LASSO Cox regression analysis, and correlation analysis was conducted between the signature and clinical characteristics. ICGC cohort and GSE14520 were applied for external validations of the predictive risk model. The relationship between immune cell infiltration, TMB, MSI, therapeutic sensitivity of immune checkpoint inhibitors, targeted drugs, and the risk model were assessed by bioinformatics analysis in HCC patients. RESULTS: A risk predictive model consisting of 3 immune-related gene pairs was constructed and the risk score was proved as an independent prognostic factor for HCC patients combining the TCGA cohort. This predictive model exhibited a positive correlation with tumor size (p < 0.01) and tumor stage (TNM) (p < 0.001) in the chi-square test. The predictive power was verified by external validations (ICGC and GSE14520). The risk score clearly correlated with immune cell infiltration, MSI, immune checkpoints, and markers of angiogenesis. CONCLUSIONS: Our research established a risk predictive model based on 3 immune-related gene pairs and explored its relationship with immune characteristics, which might help to assess the prognosis and treatment sensitivity to immune and targeted therapy of HCC patients.


Assuntos
Carcinoma Hepatocelular , Neoplasias Hepáticas , Biomarcadores Tumorais/genética , Carcinoma Hepatocelular/genética , Carcinoma Hepatocelular/patologia , Biologia Computacional , Humanos , Neoplasias Hepáticas/tratamento farmacológico , Neoplasias Hepáticas/genética , Neoplasias Hepáticas/patologia , Prognóstico
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