1,385 research outputs found

    Cross-Platform Comparison of Microarray-Based Multiple-Class Prediction

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    High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets

    Genome-Wide SNP-genotyping array to study the evolution of the human pathogen Vibrio vulnificus Biotype 3

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    Vibrio vulnificus is an aquatic bacterium and an important human pathogen. Strains Of V. vulnificus are classified into three different biotypes. The newly emerged biotype 3 has been found to be clonal and restricted to Israel. In the family Vibrionaceae , horizontal gene transfer is the main mechanism responsible for the emergence of new pathogen groups. To better understand the evolution of the bacterium, and in particular to trace the evolution of biotype 3, we performed genome-wide SNP genotyping of 254 clinical and environmental V. vulnificus isolates with worldwide distribution recovered over a 30-year period, representing all phylogeny groups. A custom single-nucleotide polymorphism (SNP) array implemented on the Illumina GoldenGate platform was developed based on 570 SNPs randomly distributed throughout the genome. In general, the genotyping results divided the V. vulnificus species into three main phylogenetic lineages and an additional subgroup, clade B, consisting of environmental and clinical isolates from Israel. Data analysis suggested that 69% of biotype 3 SNPs are similar to SNPs from clade B, indicating that biotype 3 and clade B have a common ancestor. The rest of the biotype 3 SNPs were scattered along the biotype 3 genome, probably representing multiple chromosomal segments that may have been horizontally inserted into the clade B recipient core genome from other phylogroups or bacterial species sharing the same ecological niche. Results emphasize the continuous evolution of V. vulnificus and support the emergence of new pathogenic groups within this species as a recurrent phenomenon. Our findings contribute to a broader understanding of the evolution of this human pathogen

    A semi-nonparametric mixture model for selecting functionally consistent proteins

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    Background High-throughput technologies have led to a new era of proteomics. Although protein microarray experiments are becoming more common place there are a variety of experimental and statistical issues that have yet to be addressed, and that will carry over to new high-throughput technologies unless they are investigated. One of the largest of these challenges is the selection of functionally consistent proteins. Results We present a novel semi-nonparametric mixture model for classifying proteins as consistent or inconsistent while controlling the false discovery rate and the false non-discovery rate. The performance of the proposed approach is compared to current methods via simulation under a variety of experimental conditions. Conclusions We provide a statistical method for selecting functionally consistent proteins in the context of protein microarray experiments, but the proposed semi-nonparametric mixture model method can certainly be generalized to solve other mixture data problems. The main advantage of this approach is that it provides the posterior probability of consistency for each protein

    Subcellular localization of NEDD9 and HMB45 with AQUA technology to distinguish Spitz nevi from melanoma

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    SUBCELLULAR LOCALIZATION OF NEDD9 AND HMB45 WITH AQUA TECHNOLOGY TO DISTINGUISH SPITZ NEVI FROM MELANOMA. Matthew C. McRae, Rossitza Lasova, Bonnie Gould-Rothberg, David Rimm (Sponsored by Deepak Narayan). Section of Plastic Surgery, Department of Surgery, Yale University School of Medicine, New Haven, CT. Our hypothesis is that the expression level and subcellular localization of HMB45 and NEDD9 as demonstrated by the ln(nuclear/non-nuclear) Automated Quantitative Analysis (AQUA) score, defined as the subcellular AQUA ratio, will be consistently altered between benign nevi and melanoma and between Spitz nevi and Spitzoid melanoma. Our specific aims are to assess quantitative expression and subcellular localization of HMB45 and NEDD9 to aid in the diagnosis of benign Spitz nevi and malignant Spitzoid melanoma. This remains a vexing clinical problem with important implications for treatment and patient care. The quantitative expression and subcellular AQUA ratio will be assessed in the following samples: benign derived versus malignant derived cell lines, human benign nevi, human primary melanoma, human metastatic melanoma, typical Spitz nevi, atypical Spitz nevi and Spitzoid melanoma. AQUA was used to quantify protein expression levels in subcellular compartments using fluorescence-based immunohistochemistry. Tissue Microarrays (TMA) analysis was used for cell line, benign nevi and malignant melanoma while whole section analysis was used for Spitz nevi and Spitzoid melanoma. NEDD9 subcellular AQUA ratio was significantly reduced in primary melanoma (mean=-0.645, std dev=0.29) versus benign nevi (mean = -0.429, std dev=0.108) on YTMA98-2 (p=0.0086), significantly reduced in melanoma metastases (mean=-0.482, std dev=0.149) versus benign nevi (mean= -0.342, std dev=0.159) on YTMA66A (p\u3c0.0001), and significantly reduced in primary melanoma (mean= -0.435, std. dev.=0.185) and melanoma metastases (mean= -0.42, std. dev.= 0.188) versus benign nevi (mean= -0.319, std. dev.= 0.141) in SPORE84 array (p=0.0003, Tukey/Kramer post-hoc significance p\u3c0.05). HMB45 subcellular AQUA ratio was significantly reduced in primary melanoma (mean=-0.463, std. dev.=0.264) versus benign nevi (mean=-0.159, std. dev.=0.158) on YTMA 98-2 array (p=0.0001). On whole section analysis, the HMB45 and NEDD9 subcellular AQUA ratio shared a similar distribution between Spitz nevi, atypical Spitz nevi and Spitzoid melanoma. Subcellular localization using the subcellular AQUA ratio of HMB45 and NEDD9 defines benign nevi from melanoma on TMA but is not useful in discriminating between benign Spitz nevi and melanoma with Spitzoid features. The maximum HMB45 AQUA score in the tumor mask in a single 20X high-powered field of a whole tissue section was deemed promising on discovery analysis at differentiating between Spitz nevi and melanoma with Spitzoid features (p=0.007, receiver operating characteristic area under curve 0.711) but requires validation on an independent cohort

    Glioma cells on the run – the migratory transcriptome of 10 human glioma cell lines

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    <p>Abstract</p> <p>Background</p> <p>Glioblastoma multiforme (GBM) is the most common primary intracranial tumor and despite recent advances in treatment regimens, prognosis for affected patients remains poor. Active cell migration and invasion of GBM cells ultimately lead to ubiquitous tumor recurrence and patient death.</p> <p>To further understand the genetic mechanisms underlying the ability of glioma cells to migrate, we compared the matched transcriptional profiles of migratory and stationary populations of human glioma cells. Using a monolayer radial migration assay, motile and stationary cell populations from seven human long term glioma cell lines and three primary GBM cultures were isolated and prepared for expression analysis.</p> <p>Results</p> <p>Gene expression signatures of stationary and migratory populations across all cell lines were identified using a pattern recognition approach that integrates <it>a priori </it>knowledge with expression data. Principal component analysis (PCA) revealed two discriminating patterns between migrating and stationary glioma cells: i) global down-regulation and ii) global up-regulation profiles that were used in a proband-based rule function implemented in GABRIEL to find subsets of genes having similar expression patterns. Genes with up-regulation pattern in migrating glioma cells were found to be overexpressed in 75% of human GBM biopsy specimens compared to normal brain. A 22 gene signature capable of classifying glioma cultures based on their migration rate was developed. Fidelity of this discovery algorithm was assessed by validation of the invasion candidate gene, connective tissue growth factor (CTGF). siRNA mediated knockdown yielded reduced <it>in vitro </it>migration and <it>ex vivo </it>invasion; immunohistochemistry on glioma invasion tissue microarray confirmed up-regulation of CTGF in invasive glioma cells.</p> <p>Conclusion</p> <p>Gene expression profiling of migratory glioma cells induced to disperse <it>in vitro </it>affords discovery of genomic signatures; selected candidates were validated clinically at the transcriptional and translational levels as well as through functional assays thereby underscoring the fidelity of the discovery algorithm.</p

    Diffuse large B-cell lymphoma - tumour characteristics on RNA and protein level associated with prognosis

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    Diffuse large B-cell lymphoma (DLBCL) is the most frequent lymphoma subtype. In Sweden 450 new cases are diagnosed annually. With modern anthracycline-containing chemotherapy DLBCL is potentially curable, with an estimated overall cure rate of approximately 50% for patients with advanced stage disease. Through molecular profiling of DLBCL the ?cell-of-origin concept? has been established: patients with tumours expressing genes characteristic of germinal center B-cells, ?GC-profile? has a significantly better survival than patients with tumors expressing genes normally induced during in vitro activation of peripheral blood B-cells, ?ABC-profile?. The first study (n=125) aimed to identify a protein pattern that could be used for discriminating germinal center derived (GC) and activated B-cell like (ABC)/non-GC DLBCL, using immunohistochemistry (IHC). BCL6, CD10 and CD40 were chosen as markers of a GC-phenotype, CD23 as a marker of pre/early GC-origin and CD138 as a marker of post-GC origin (i.e non-GC). No prognostically different subgroups, corresponding to GC or ABC (non-GC) could be identified. A new finding was the positive prognostic impact of CD23 and CD40 expression. In the second study (n=125) the prognostic effect of CD40, but not CD23, was confirmed. The effect of CD40 effect could not be explained by association with the GC-phenotype or by an enhanced autologous tumour response, as detected by tumour infiltrating helper and cytotoxic T-lymphocytes. The prognostic effect of a GC versus non-GC phenotype according to Hans et al (Blood 2004) was confirmed. The third study (n=122) identified the tissue microarray technique to be unreliable for immunohistochemical detection in GC vs. non-GC phenotypes, mostly due to difficulties interpreting BCL6 status. In the fourth study tumours from patients with cured (n=24) versus primary chemotherapy-refractory DLBCL (n=13), were investigated with respect to gene expression profiles, using spotted 55K oligonucleotide arrays produced in Lund. The genes that most differed between chemotherapy sensitive and refractory tumours mainly coded for proteins expressed by cells in the tumour microenvironment, and not by the tumour cells themselves. Confirmative IHC showed that the frequency of tumour infiltrating lymphocytes, macrophages and reactive cells expressing proteolytic and pro-inflammatory proteins were higher in the chemo-sensitive cohort, indicating that the microenvironment has an impact on the response to chemotherapy in DLBCL

    Temperature Dependent Control of the R27 Conjugative Plasmid Genes

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    Conjugation of R27 plasmid is thermoregulated, being promoted at 25Β°C and repressed at 37Β°C. Previous studies identified plasmid-encoded regulators, HtdA, TrhR and TrhY, that control expression of conjugation-related genes (tra). Moreover, the nucleoid-associated protein H-NS represses conjugation at non-permissive temperature. A transcriptomic approach has been used to characterize the effect of temperature on the expression of the 205 R27 genes. Many of the 35 tra genes, directly involved in plasmid-conjugation, were upregulated at 25Β°C. However, the majority of the non-tra R27 genes many of them with unknown function were more actively expressed at 37Β°C. The role of HtdA, a regulator that causes repression of the R27 conjugation by counteracting TrhR/TrhY mediated activation of tra genes, has been investigated. Most of the R27 genes are severely derepressed at 25Β°C in an htdA mutant, suggesting that HtdA is involved also in the repression of R27 genes other than the tra genes. Interestingly, the effect of htdA mutation was abolished at non-permissive temperature, indicating that the HtdA-TrhR/TrhY regulatory circuit mediates the environmental regulation of R27 gene expression. The role of H-NS in the proposed model is discussed

    Expression cartography of human tissues using self organizing maps

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    Background: The availability of parallel, high-throughput microarray and sequencing experiments poses a challenge how to best arrange and to analyze the obtained heap of multidimensional data in a concerted way. Self organizing maps (SOM), a machine learning method, enables the parallel sample- and gene-centered view on the data combined with strong visualization and second-level analysis capabilities. The paper addresses aspects of the method with practical impact in the context of expression analysis of complex data sets.&#xd;&#xa;Results: The method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues). SOM mapping reduces the dimension of expression data from ten thousands of genes to a few thousands of metagenes where each metagene acts as representative of a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of gene sets well corresponding to molecular processes in the respective tissues. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering provide a better signal-to-noise ratio and a better representativeness of the method if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues into essentially three clusters containing nervous, immune system and the remaining tissues. &#xd;&#xa;Conclusions: The global view on the behavior of a few well-defined modules of correlated and differentially expressed genes is more intuitive and more informative than the separate discovery of the expression levels of hundreds or thousands of individual genes. The metagene approach is less sensitive to a priori selection of genes. It can detect a coordinated expression pattern whose components would not pass single-gene significance thresholds and it is able to extract context-dependent patterns of gene expression in complex data sets.&#xd;&#xa
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