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Staphylococcus aureus can cause a wide variety of diseases, which are attributed to its large repertoire of virulence factors, many of which are under the control of two component sensing systems (TCS). These systems allow the pathogen to alter and regulate its gene expression profile under the environmental stimuli. A simple TCS system is composed of a membrane associated histidine kinase and response regulator which is mainly present in cytosol. There is a total of sixteen such systems in Staphylococcus aureus which are involved in diverse signaling cascades including response to antimicrobial agents, nutrient sensing, chemotaxis, virulent gene regulation and biofilm formations. Most of these systems are interlinked and influence each other to regulate virulence profile of the pathogen. In this study, we summarize our current understanding of these systems and their different component in methicillin resistant Staphylococcus aureus which will have important implications in structure-based approaches for designing novel class of small molecule inhibitors to combat Staphylococcal infections. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cancer nowadays, and is also one of the leading causes of death among women worldwide. In recent years, with the extensive application of high-throughput sequencing and gene expression profiling, it has become more and more convenient to dig and identify tumor molecular markers. This study aims to find potential biomarkers related to breast cancer molecular subtyping and prognosis, and provide evidence for diagnosis and treatment of breast cancer. This study combined the data of several large breast cancer databases and conducted in-depth analysis. Kaplan-Meier survival analysis and Log-Rank test were used for the prognosis of patients with different subtypes of breast cancer. By analyzing the data collected, it is found that BRCA1 and SQLE were generally negatively correlated with the prognosis of breast cancer patients, ER/PR positive patients, and HER2 negative patients, and had significant negative correlation with prognosis in LN- and Luminal A breast cancer. As an adverse-prognostic factor, BRCA1 and SQLE have strong prognostic significance in patients with mild breast cancer and can be potential tumor markers. [ABSTRACT FROM AUTHOR]

Microarray or gene expression profiling is conducted in a single experiment with different types of cells or tissue samples to evaluate and compare the extent of gene expression. The role of classifying the sample in question is more promising as the microarray dataset is disrupted due to the dimensional component, with a limited number of samples together with incorrect and noise genes. This is most important for the screening and diagnosis of a sample of breast cancer. The method of gene selection is currently being formulated to classify a few numbers of significant genes that are correlated with the extremely predictive process in the classification field. To carry out this method, a revolutionary genetic selection algorithm, namely the minimal Redundancy Maximal Significance (mRMR), is devised and integrated with the Gene Weight Imperialist Competitive Algorithm (GWICA), mRMR-GWICA, to classify insightful genes from the microarray-containing profile. The ground-breaking approach relies on the parallel Progressive Inductive Subspace Ensemble Clustering (PPISEC) algorithm to measure the precision of the classification of the known genes. PPISEC-WICA algorithm has three key steps: Improved Support Vector Machine (ISVM) classifier and Incremental Ensemble Member Chosen (IEMC) by GWICA are used to pick the centroid values and to execute the gene expression data clustering a structured cut algorithm is suggested. Experimental findings reveal that the formulated PPISEC system performs well on data containing the expression of the gene for breast cancer relative to conventional clustering ensemble approaches. This design is applied in a dataset comprising the gene expression dataset (GSE45827) which is collected from breast-cancer-gene-expression-cumida. The outputs of the clustering method are determined based on certain metrics such as precision, recall, f-measurement, accuracy, Adjusted Rand Index (ARI), and Normalized Mutual Information (NMI). The clustering design is implemented by implementing the MATLAB simulation environment in the R2014a edition. [ABSTRACT FROM AUTHOR]

An innate immune defect is a defect in the innate immune response that reduces the response to infection, this can occurs in genes important for activation regulation and proliferation of the innate immune cells or pathways important for the function of innate immunity. The purpose of this study was to identify novel biomarkers of interferon Receptor 7 through bioinformatics analysis and elucidate the possible molecular mechanism. The GSE 66486 datasets containing microarray data from IRF7 and UNC93B patients and healthy controls were downloaded from the GEO database and analyzed by the GEO2R web tool to obtain different expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein-protein interaction (PPI), and Biological Networks Gene Oncology tool (BiNGO) were then performed to elucidate the molecular mechanism of IRF7. A total of 490 DEGs were identified, of which 14 were hub genes, and involved in ribosome biogenesis, rRNA processing, gene expression, mRNA processing, nuclear lumen, intracellular non-membrane-bounded organelle, nucleoplasm, small-subunit processome, antigen processing and presentation pathway, and ribosome biogenesis in eukaryotes. Antigen processing and presentation pathway, and ribosome biogenesis in eukaryotes possibly form the basis of IRF7 or UNC93B disorders, while our study provides a list of genes and pathways that are disrupted in IRF7/UNC93B, which has the potential to be used in the clinic for diagnosis and targeted therapy of such disorders in future. [ABSTRACT FROM AUTHOR]

Toll-IL1 receptor (TLR) and TIR signaling pathway genes (IRAK4, MyD88) deficiencies are an inborn error of the innate immune system. Defect in these two genes results in improper downstream signaling of the TLR and TIR pathways which is the important step for the activation of innate and adaptive immune systems. This study aimed to complement the differentially expressed genes (DEGs) innate immune response of patients with inborn errors in Toll-IL1 receptor signaling pathway (IRAK4, MyD88 deficiencies) compared to healthy subjects, identify their functions and molecular pathways. In order to identify key genes, enriched pathways, and important modules in the patients with such inborn innate immune system errors data for gene expression profiles (GSE25742) were downloaded from the GEO database, and bioinformatics analysis was made. In total 500 DEGs were identified. Among them, 191 (38.2%) genes were up-regulated and 309 (61.8%) genes were down-regulated. Gene ontology (GO) and KEGG pathway enrichment analysis of target genes was performed. Furthermore, we reviewed some relevant core genes such as TNF, IL1B, TLR2, MMP9, SPL1, ITAGAX, HCK, Lyn, CXCL1, and FPR2 by using gene-gene interaction network analysis, which may contribute to a better understanding of the molecular mechanisms of the inborn error of innate immunity or TLR signaling. Whereas six clusters were identified by MCODE. These genes and pathways repotted might provide a new perspective for revealing the underlying pathological mechanisms and therapy strategy for inborn error in Tool like receptor signaling pathways and other inborn errors of the innate immune system. [ABSTRACT FROM AUTHOR]

Cancer is one of the dreadful diseases, which causes a considerable death rate in humans. Cancer is featured by an irregular, unmanageable growth that may demolish and attack neighboring healthy body tissues or somewhere else in the body. Microarray based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. In recent years, DNA microarray technique has gained more attraction in both scientific and in industrial fields. It showed great importance in determining the informative genes that can cause the cancer. This led to improvements in early cancer diagnosis and in giving effective chemotherapy treatment. Studding cancer microarray gene expression data is a challenging task because microarray is high dimensional-low sample dataset with a lot of noisy or irrelevant genes and missing data. In this paper, we conduct a comprehensive study that focuses on exploring the main objectives and approaches that have been applied using cancer microarray gene expression profile. We proceed by making a classification for all approaches, and then conclude by investigating the most efficient approaches that can be used in this field. [ABSTRACT FROM AUTHOR]

Recently, there has been an increasing interest in mining time series databases with a focus on data representation. We propose a hybrid statistical and temporal logic model to yield search pattern that can be used for pattern matching and database search to identify novel and common patterns in temporal expression experiments. The method accounts for various challenges that can be found in publicly available gene expression databases. [ABSTRACT FROM AUTHOR]

Recent studies have identified some genes related to cancer phenotypes, but lack of comprehensive clinical information. Here we present an integrated data mining method to find associations between genes and multiple clinical conditions of breast cancer by using gene expression data from Gene Expression Omnibus. The associated modules of genes and clinical conditions were built and some potential related genes were suggested. [ABSTRACT FROM AUTHOR]