475 research outputs found

    Exome sequencing identifies nonsegregating nonsense ATM and PALB2 variants in familial pancreatic cancer.

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    We sequenced 11 germline exomes from five families with familial pancreatic cancer (FPC). One proband had a germline nonsense variant in ATM with somatic loss of the variant allele. Another proband had a nonsense variant in PALB2 with somatic loss of the variant allele. Both variants were absent in a relative with FPC. These findings question the causal mechanisms of ATM and PALB2 in these families and highlight challenges in identifying the causes of familial cancer syndromes using exome sequencing

    Grouping time series by pairwise measures of redundancy

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    A novel approach is proposed to group redundant time series in the frame of causality. It assumes that (i) the dynamics of the system can be described using just a small number of characteristic modes, and that (ii) a pairwise measure of redundancy is sufficient to elicit the presence of correlated degrees of freedom. We show the application of the proposed approach on fMRI data from a resting human brain and gene expression profiles from HeLa cell culture.Comment: 4 pages, 8 figure

    Weak pairwise correlations imply strongly correlated network states in a neural population

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    Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint that complicated, higher order interactions among large groups of elements play an important role. In the vertebrate retina, we show that weak correlations between pairs of neurons coexist with strongly collective behavior in the responses of ten or more neurons. Surprisingly, we find that this collective behavior is described quantitatively by models that capture the observed pairwise correlations but assume no higher order interactions. These maximum entropy models are equivalent to Ising models, and predict that larger networks are completely dominated by correlation effects. This suggests that the neural code has associative or error-correcting properties, and we provide preliminary evidence for such behavior. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons.Comment: Full account of work presented at the conference on Computational and Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt Lake City, Utah (http://cosyne.org

    Microarray gene expression profiling and analysis in renal cell carcinoma

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    BACKGROUND: Renal cell carcinoma (RCC) is the most common cancer in adult kidney. The accuracy of current diagnosis and prognosis of the disease and the effectiveness of the treatment for the disease are limited by the poor understanding of the disease at the molecular level. To better understand the genetics and biology of RCC, we profiled the expression of 7,129 genes in both clear cell RCC tissue and cell lines using oligonucleotide arrays. METHODS: Total RNAs isolated from renal cell tumors, adjacent normal tissue and metastatic RCC cell lines were hybridized to affymatrix HuFL oligonucleotide arrays. Genes were categorized into different functional groups based on the description of the Gene Ontology Consortium and analyzed based on the gene expression levels. Gene expression profiles of the tissue and cell line samples were visualized and classified by singular value decomposition. Reverse transcription polymerase chain reaction was performed to confirm the expression alterations of selected genes in RCC. RESULTS: Selected genes were annotated based on biological processes and clustered into functional groups. The expression levels of genes in each group were also analyzed. Seventy-four commonly differentially expressed genes with more than five-fold changes in RCC tissues were identified. The expression alterations of selected genes from these seventy-four genes were further verified using reverse transcription polymerase chain reaction (RT-PCR). Detailed comparison of gene expression patterns in RCC tissue and RCC cell lines shows significant differences between the two types of samples, but many important expression patterns were preserved. CONCLUSIONS: This is one of the initial studies that examine the functional ontology of a large number of genes in RCC. Extensive annotation, clustering and analysis of a large number of genes based on the gene functional ontology revealed many interesting gene expression patterns in RCC. Most notably, genes involved in cell adhesion were dominantly up-regulated whereas genes involved in transport were dominantly down-regulated. This study reveals significant gene expression alterations in key biological pathways and provides potential insights into understanding the molecular mechanism of renal cell carcinogenesis

    svdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification

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    <p>Abstract</p> <p>Background</p> <p>Comparative analysis of gene expression profiling of multiple biological categories, such as different species of organisms or different kinds of tissue, promises to enhance the fundamental understanding of the universality as well as the specialization of mechanisms and related biological themes. Grouping genes with a similar expression pattern or exhibiting co-expression together is a starting point in understanding and analyzing gene expression data. In recent literature, gene module level analysis is advocated in order to understand biological network design and system behaviors in disease and life processes; however, practical difficulties often lie in the implementation of existing methods.</p> <p>Results</p> <p>Using the singular value decomposition (SVD) technique, we developed a new computational tool, named svdPPCS (<b>SVD</b>-based <b>P</b>attern <b>P</b>airing and <b>C</b>hart <b>S</b>plitting), to identify conserved and divergent co-expression modules of two sets of microarray experiments. In the proposed methods, gene modules are identified by splitting the two-way chart coordinated with a pair of left singular vectors factorized from the gene expression matrices of the two biological categories. Importantly, the cutoffs are determined by a data-driven algorithm using the well-defined statistic, SVD-p. The implementation was illustrated on two time series microarray data sets generated from the samples of accessory gland (ACG) and malpighian tubule (MT) tissues of the line W<sup>118 </sup>of <it>M. drosophila</it>. Two conserved modules and six divergent modules, each of which has a unique characteristic profile across tissue kinds and aging processes, were identified. The number of genes contained in these models ranged from five to a few hundred. Three to over a hundred GO terms were over-represented in individual modules with FDR < 0.1. One divergent module suggested the tissue-specific relationship between the expressions of mitochondrion-related genes and the aging process. This finding, together with others, may be of biological significance. The validity of the proposed SVD-based method was further verified by a simulation study, as well as the comparisons with regression analysis and cubic spline regression analysis plus PAM based clustering.</p> <p>Conclusions</p> <p>svdPPCS is a novel computational tool for the comparative analysis of transcriptional profiling. It especially fits the comparison of time series data of related organisms or different tissues of the same organism under equivalent or similar experimental conditions. The general scheme can be directly extended to the comparisons of multiple data sets. It also can be applied to the integration of data sets from different platforms and of different sources.</p

    Multivariate curve resolution of time course microarray data

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    BACKGROUND: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. RESULTS: In this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data. CONCLUSION: Profiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements

    Hydrodilatation, corticosteroids and adhesive capsulitis: A randomized controlled trial

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    <p>Abstract</p> <p>Background</p> <p>Hydrodilatation of the glenohumeral joint is by several authors reported to improve shoulder pain and range of motion for patients with adhesive capsulitis. Procedures described often involve the injection of corticosteroids, to which the reported treatment effects may be attributed. Any important contribution arising from the hydrodilatation procedure itself remains to be demonstrated.</p> <p>Methods</p> <p>In this randomized trial, a hydrodilatation procedure including corticosteroids was compared with the injection of corticosteroids without dilatation. Patients were given three injections with two-week intervals, and all injections were given under fluoroscopic guidance. Outcome measures were the Shoulder Pain and Disability Index (SPADI) and measures of active and passive range of motion. Seventy-six patients were included and groups were compared six weeks after treatment. The study was designed as an open trial.</p> <p>Results</p> <p>The groups showed a rather similar degree of improvement from baseline. According to a multiple regression analysis, the effect of dilatation was a mean improvement of 3 points (confidence interval: -5 to 11) on the SPADI 0–100 scale. T-tests did not demonstrate any significant between-group differences in range of motion.</p> <p>Conclusion</p> <p>This study did not identify any important treatment effects resulting from three hydrodilatations that included steroid compared with three steroid injections alone.</p> <p>Trial registration</p> <p>The study is registered in Current Controlled Trials with the registration number ISRCTN90567697.</p

    Frequent Missense and Insertion/Deletion Polymorphisms in the Ovine Shadoo Gene Parallel Species-Specific Variation in PrP

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    BACKGROUND: The cellular prion protein PrP(C) is encoded by the Prnp gene. This protein is expressed in the central nervous system (CNS) and serves as a precursor to the misfolded PrP(Sc) isoform in prion diseases. The prototype prion disease is scrapie in sheep, and whereas Prnp exhibits common missense polymorphisms for V136A, R154H and Q171R in ovine populations, genetic variation in mouse Prnp is limited. Recently the CNS glycoprotein Shadoo (Sho) has been shown to resemble PrP(C) both in a central hydrophobic domain and in activity in a toxicity assay performed in cerebellar neurons. Sho protein levels are reduced in prion infections in rodents. Prompted by these properties of the Sho protein we investigated the extent of natural variation in SPRN. PRINCIPAL FINDINGS: Paralleling the case for ovine versus human and murine PRNP, we failed to detect significant coding polymorphisms that alter the mature Sho protein in a sample of neurologically normal humans, or in diverse strains of mice. However, ovine SPRN exhibited 4 missense mutations and expansion/contraction in a series of 5 tandem Ala/Gly-containing repeats R1-R5 encoding Sho's hydrophobic domain. A Val71Ala polymorphism and polymorphic expansion of wt 67(Ala)(3)Gly70 to 67(Ala)(5)Gly72 reached frequencies of 20%, with other alleles including Delta67-70 and a 67(Ala)(6)Gly73 expansion. Sheep V71, A71, Delta67-70 and 67(Ala)(6)Gly73 SPRN alleles encoded proteins with similar stability and posttranslational processing in transfected neuroblastoma cells. SIGNIFICANCE: Frequent coding polymorphisms are a hallmark of the sheep PRNP gene and our data indicate a similar situation applies to ovine SPRN. Whether a common selection pressure balances diversity at both loci remains to be established
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