1,655,135 research outputs found

    Gene Expression Patterns Distinguish Mortality Risk in Patients with Postsurgical Shock

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    Producci贸n Cient铆ficaNowadays, mortality rates in intensive care units are the highest of all hospital units. However, there is not a reliable prognostic system to predict the likelihood of death in patients with postsurgical shock. Thus, the aim of the present work is to obtain a gene expression signature to distinguish the low and high risk of death in postsurgical shock patients. In this sense, mRNA levels were evaluated by microarray on a discovery cohort to select the most differentially expressed genes between surviving and non-surviving groups 30 days after the operation. Selected genes were evaluated by quantitative real-time polymerase chain reaction (qPCR) in a validation cohort to validate the reliability of data. A receiver-operating characteristic analysis with the area under the curve was performed to quantify the sensitivity and specificity for gene expression levels, which were compared with predictions by established risk scales, such as acute physiology and chronic health evaluation (APACHE) and sequential organ failure assessment (SOFA). IL1R2, CD177, RETN, and OLFM4 genes were upregulated in the non-surviving group of the discovery cohort, and their predictive power was confirmed in the validation cohort. This work offers new biomarkers based on transcriptional patterns to classify the postsurgical shock patients according to low and high risk of death. The results present more accuracy than other mortality risk scores.Instituto de Salud Carlos III (grant PI15/01451)Junta de Castilla y Le贸n (grant 1255/A/16)Universidad de Valladolid - Fondo Europeo de Desarrollo Regional (grant VA321P18

    Impact of Nuclear Domains On Gene Expression and Plant Traits

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    Multiples lines of evidence indicate that spatial 3D organisation nuclear DNA is critical in adapting to different environmental conditions and the Impact of Nuclear Domains On Gene Expression and Plant Traits (INDEPTH) network aims to decipher how nuclear architecture, chromatin organisation and gene expression are connected and modified in response to internal and external cues

    Gene Expression Commons: an open platform for absolute gene expression profiling.

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    Gene expression profiling using microarrays has been limited to comparisons of gene expression between small numbers of samples within individual experiments. However, the unknown and variable sensitivities of each probeset have rendered the absolute expression of any given gene nearly impossible to estimate. We have overcome this limitation by using a very large number (>10,000) of varied microarray data as a common reference, so that statistical attributes of each probeset, such as the dynamic range and threshold between low and high expression, can be reliably discovered through meta-analysis. This strategy is implemented in a web-based platform named "Gene Expression Commons" (https://gexc.stanford.edu/) which contains data of 39 distinct highly purified mouse hematopoietic stem/progenitor/differentiated cell populations covering almost the entire hematopoietic system. Since the Gene Expression Commons is designed as an open platform, investigators can explore the expression level of any gene, search by expression patterns of interest, submit their own microarray data, and design their own working models representing biological relationship among samples

    Generalized gene co-expression analysis via subspace clustering using low-rank representation

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    BACKGROUND: Gene Co-expression Network Analysis (GCNA) helps identify gene modules with potential biological functions and has become a popular method in bioinformatics and biomedical research. However, most current GCNA algorithms use correlation to build gene co-expression networks and identify modules with highly correlated genes. There is a need to look beyond correlation and identify gene modules using other similarity measures for finding novel biologically meaningful modules. RESULTS: We propose a new generalized gene co-expression analysis algorithm via subspace clustering that can identify biologically meaningful gene co-expression modules with genes that are not all highly correlated. We use low-rank representation to construct gene co-expression networks and local maximal quasi-clique merger to identify gene co-expression modules. We applied our method on three large microarray datasets and a single-cell RNA sequencing dataset. We demonstrate that our method can identify gene modules with different biological functions than current GCNA methods and find gene modules with prognostic values. CONCLUSIONS: The presented method takes advantage of subspace clustering to generate gene co-expression networks rather than using correlation as the similarity measure between genes. Our generalized GCNA method can provide new insights from gene expression datasets and serve as a complement to current GCNA algorithms

    Origins of Binary Gene Expression in Post-transcriptional Regulation by MicroRNAs

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    MicroRNA-mediated regulation of gene expression is characterised by some distinctive features that set it apart from unregulated and transcription factor-regulated gene expression. Recently, a mathematical model has been proposed to describe the dynamics of post-transcriptional regulation by microRNAs. The model explains the observations made in single cell experiments quite well. In this paper, we introduce some additional features into the model and consider two specific cases. In the first case, a non-cooperative positive feedback loop is included in the transcriptional regulation of the target gene expression. In the second case, a stochastic version of the original model is considered in which there are random transitions between the inactive and active expression states of the gene. In the first case we show that bistability is possible in a parameter regime, due to the presence of a non-linear protein decay term in the gene expression dynamics. In the second case, we derive the conditions for obtaining stochastic binary gene expression. We find that this type of gene expression is more favourable in the case of regulation by microRNAs as compared to the case of unregulated gene expression. The theoretical predictions relating to binary gene expression are experimentally testable.Comment: 10 Pages, 5 Figure

    CagA and VacA Gene Expression in Helicobacter Pylori Infected Patients in Dr. Soetomo General Hospital

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    Marshall and Warren had discovered helicobacter pylori in 1982 and known as the main pathogen caused infection on human's stomach. Helicobacter pylori is a bacillus spiral and gram negative bacteria which is motile as it has almost six flagella on one side of its body (unipolar). There are strain type I, intermediate and type II. Strain type I has cytotoxin associated gene A (cagA) and vacuolating cytotoxin gene A (vacA) while strain type II has vacuolating cytotoxin gene A (vacA). Because of cag pathogenicity island (PAI), strain type I has the tendency to cause the infection become more Malignant. This study was conducted by using descriptive purposeful sampling method on patients in endoscopy department of internal medicine in the division of hepatology gastroentero Dr. Soetomo starting from October 20 until November 25, 2015. The aim of this study is to determine whether the stool sample shows cagA gene and or vacA gene. The data was proceed by observation through the results of PCR assays to look at the genes that are expressed by Helicobacter pylori. DNA was extracted from stool by using QIAamp (Qiagen) stool kit. Results of the study show only one patient positive for vacA gene while cagA gene is none from ten patients. DNA examinations with different concentrations and temperatures also show the same results. One sample from the stool specimen shows positive for strain type II, indicates it only has vacA gene. PCR examination through gastric biopsy is known has higher specificity

    Lentiviral vectors with amplified beta cell-specific gene expression.

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    An important goal of gene therapy is to be able to deliver genes, so that they express in a pattern that recapitulates the expression of an endogenous cellular gene. Although tissue-specific promoters confer selectivity, in a vector-based system, their activity may be too weak to mediate detectable levels in gene-expression studies. We have used a two-step transcriptional amplification system to amplify gene expression from lentiviral vectors using the human insulin promoter. In this system, the human insulin promoter drives expression of a potent synthetic transcription activator (the yeast GAL4 DNA-binding domain fused to the activation domain of the Herpes simplex virus-1 VP16 activator), which in turn activates a GAL4-responsive promoter, driving the enhanced green fluorescent protein reporter gene. Vectors carrying the human insulin promoter did not express in non-beta-cell lines, but expressed in murine insulinoma cell lines, indicating that the human insulin promoter was capable of conferring cell specificity of expression. The insulin-amplifiable vector was able to amplify gene expression five to nine times over a standard insulin-promoter vector. In primary human islets, gene expression from the insulin-promoted vectors was coincident with insulin staining. These vectors will be useful in gene-expression studies that require a detectable signal and tissue specificity

    Comparative Enumeration Gene Expression

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    This paper is about differential gene expression measured by transcript counting methods such as SAGE or MPSS. It introduces two significance tests for detection of differential expressed tags: frequentist and Bayesian. Under the frequentist view, it is proposed a test that computes the critical level as a function of each tag total frequency. Under the Bayesian view the Full Bayesian Significance Test is used considering the logistic normal distribution. The two proposed significance levels, the frequentist and the Bayesian, are compared for a data set with four libraries. The linking function between them is a Beta distribution function with mean 0.39 and standard deviation 0.30
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