Biomedical subjects

Michael Nebozhyn

Publications and source records attributed to Michael Nebozhyn.

5 recordsLinked to original sources

PubMed2006

Microarray data on gene modulation by HIV-1 in immune cells: 2000-2006.

Here, we review 34 HIV microarray studies in human immune cells over the period of 2000-March 2006 with emphasis on analytical approaches used and conceptual advances on HIV modulation of target cells (CD4 T cell, macrophage) and nontargets such as NK cell, B cell, and dendritic cell subsets. Results to date address advances on gene modulation associated with immune dysregulation, susceptibility to apoptosis, virus replication, and viral persistence following in vitro or in vivo infection/exposure to HIV-1 virus or HIV-1 accessory proteins. In addition to gene modulation associated with known functional correlates of HIV infection and replication (e.g., T cell apoptosis), microarray data have yielded novel, potential mechanisms of HIV-mediated pathogenesis such as modulation of cholesterol biosynthetic genes in CD4 T cells (relevant to virus replication and infectivity) and modulation of proteasomes and histone deacetylases in chronically infected cell lines (relevant to virus latency). Intrinsic challenges in summarizing gene modulation studies remain in development of sound approaches for comparing data obtained using different platforms and analytical tools, deriving unifying concepts to distil the large volumes of data collected, and the necessity to impose a focus for validation on a small fraction of genes. Notwithstanding these challenges, the field overall continues to demonstrate progress in expanding the pool of target genes validated to date in in vitro and in vivo datasets and understanding the functional correlates of gene modulation to HIV-1 pathogenesis in vivo.

Apoptosis↗

PubMed2006

Combining multi-species genomic data for microRNA identification using a Naive Bayes classifier.

MOTIVATION: Most computational methodologies for microRNA gene prediction utilize techniques based on sequence conservation and/or structural similarity. In this study we describe a new technique, which is applicable across several species, for predicting miRNA genes. This technique is based on machine learning, using the Naive Bayes classifier. It automatically generates a model from the training data, which consists of sequence and structure information of known miRNAs from a variety of species. RESULTS: Our study shows that the application of machine learning techniques, along with the integration of data from multiple species is a useful and general approach for miRNA gene prediction. Based on our experiments, we believe that this new technique is applicable to an extensive range of eukaryotes' genomes. Specific structure and sequence features are first used to identify miRNAs followed by a comparative analysis to decrease the number of false positives (FPs). The resulting algorithm exhibits higher specificity and similar sensitivity compared to currently used algorithms that rely on conserved genomic regions to decrease the rate of FPs.

Algorithms↗

PubMed2006

Quantitative PCR on 5 genes reliably identifies CTCL patients with 5% to 99% circulating tumor cells with 90% accuracy.

We previously identified a small number of genes using cDNA arrays that accurately diagnosed patients with Sézary Syndrome (SS), the erythrodermic and leukemic form of cutaneous T-cell lymphoma (CTCL). We now report the development of a quantitative real-time polymerase chain reaction (qRT-PCR) assay that uses expression values for just 5 of those genes: STAT4, GATA-3, PLS3, CD1D, and TRAIL. qRT-PCR data from peripheral blood mononuclear cells (PBMCs) accurately classified 88% of 17 patients with high blood tumor burden and 100% of 12 healthy controls in the training set using Fisher linear discriminant analysis (FLDA). The same 5 genes were then assayed on 56 new samples from 49 SS patients with blood tumor burdens of 5% to 99% and 69 samples from 65 new healthy controls. The average accuracy over 1000 resamplings was 90% using FLDA and 88% using support vector machine (SVM). We also tested the classifier on 14 samples from patients with CTCL with no detectable peripheral involvement and 3 patients with atopic dermatitis with severe erythroderma. The accuracy was 100% in identifying these samples as non-SS patients. These results are the first to demonstrate that gene expression profiling by quantitative PCR on a selected number of critical genes can be employed to molecularly diagnosis SS.

Dermatitis, Atopic↗

PubMed2003

Infection of U937 monocytic cells with Chlamydia pneumoniae induces extensive changes in host cell gene expression.

The effect of infection with Chlamydia pneumoniae on host messenger RNA expression in human monocytic cells with complement DNA microarrays was studied. The data chronicle a cascade of transcriptional events affecting 128 genes, many of which have not previously been reported to be affected by C. pneumoniae infection. Down-regulated genes are primarily associated with RNA and DNA metabolism, chromosomal stability, and cell-cycle regulation. Up-regulated messages include those for a variety of genes with important proinflammatory functions. Many of the up-regulated genes-including the hyaluron receptor CD44, vasoconstrictor endothelin-1, smooth muscle growth factor heparin-binding EGF-like growth factor, and fatty acid binding protein-4-had been previously described as linked to the development of atherosclerosis and other chronic inflammatory diseases. C. pneumoniae-infected monocytes can contribute to the development and progression of diseases for which acute or chronic inflammation has been shown to be important, such as atherosclerosis.

Arteriosclerosis↗

PubMed2003

Classification and prediction of survival in patients with the leukemic phase of cutaneous T cell lymphoma.

We have used cDNA arrays to investigate gene expression patterns in peripheral blood mononuclear cells from patients with leukemic forms of cutaneous T cell lymphoma, primarily Sezary syndrome (SS). When expression data for patients with high blood tumor burden (Sezary cells >60% of the lymphocytes) and healthy controls are compared by Student's t test, at P < 0.01, we find 385 genes to be differentially expressed. Highly overexpressed genes include Th2 cells-specific transcription factors Gata-3 and Jun B, as well as integrin beta1, proteoglycan 2, the RhoB oncogene, and dual specificity phosphatase 1. Highly underexpressed genes include CD26, Stat-4, and the IL-1 receptors. Message for plastin-T, not normally expressed in lymphoid tissue, is detected only in patient samples and may provide a new marker for diagnosis. Using penalized discriminant analysis, we have identified a panel of eight genes that can distinguish SS in patients with as few as 5% circulating tumor cells. This suggests that, even in early disease, Sezary cells produce chemokines and cytokines that induce an expression profile in the peripheral blood distinctive to SS. Finally, we show that using 10 genes, we can identify a class of patients who will succumb within six months of sampling regardless of their tumor burden.

Apoptosis↗