Current Genomics - Volume 4, Issue 5, 2003
Volume 4, Issue 5, 2003
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Genes Involved in Hereditary Hearing Impairment
Authors: B. Haack, M. Pfister, N. Blin and S. KupkaHereditary hearing impairment (HHI) is a heterogeneous class of disorders showing various patterns of inheritance and involving a multitude of different genes. Neurosensory hearing impairment is one of the most common human sensory disorders affecting one in 1000 children with at least 60% of cases being inherited. The mode of inheritance of non-syndromic hearing disorders can be assigned to autosomal dominant (10-15%), autosomal recessive (70%), X-linked (1-3%) and mitochondrial forms (6-37% out of all cases). Syndromal hearing defects are categorized according to the underlying defects and contain more than 700 syndromes. In daily practice, non-syndromic hearing impairment (NSHL) is predominant and accounts for up to 70% of all inherited sensorineural hearing defects.Although our knowledge of genes being involved in the development of HI accumulated during the past five years, little is known about the molecular basis of normal auditory function. To date, 24 genes for non syndromic HI and an even larger number of genes for syndromic HI have been identified. These genes play important roles for the normal inner ear: in the development, structure, ion exchange and further physiological function. This review presents a summary of known genes and new candidates, their possible role and the current state of knowledge in genetics of hereditary hearing impairment, all of them possibly expected to allow for new concepts in therapy.
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Recurrence in Bladder Cancer: A Molecular Dead End?
Authors: J.M. Bartlett, J. Edwards and K.M. GrigorTransitional cell carcinomas of the urinary bladder are frequently characterised by multiple local recurrences with a low risk of progression. The observation that a significant proportion of patients experience recurrences over many years, even decades, without ever developing aggressive life threatening disease has led to the hypothesis that recurrence is a separate clinical and molecular event in the natural history of this disease. Over recent years a number of studies have been undertaken to test this hypothesis and within the last 12-24 months candidate loci and genes have been identified which may represent such recurrence related molecular events. Within this review we have summarised the data which identifies three key loci on chromosome 9q34, 11q23 and 17q25 as associated with recurrence and sought to place these findings in the context of a multistage molecular model of bladder cancer initiation, recurrence, progression and metastasis.
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Transgenic Mouse Models for Immunosenescence
Authors: I. Nakashima, J. Du, T. Yokoyama, Y. Kawamoto, K. Ohkusu-Tsukada and K-i. IsobeAgeing-dependent dysfunctions develop for each of various cell types in living organisms. Amongst them, immunosenescence, an ageing-dependent deterioration of the immune system, seriously affects the health condition of an aged individual. The central problem in immunosenescence is a decrease in the ability of T-cells to respond to antigens for proliferation and cytokine production, accompanied by accumulation of T-cells with a memory cell phenotype (CD44-high CD45RB-low) replacing those with a naïve cell phenotype (CD44-low CD45RB-high). Recently, a transgenic mouse model and a genetically modified mouse model have been reported to display promoted immunosenescence. One is a mouse line transgenic to human CD2 promoter / enhancer-guided rabbit protein kinase C(PKC)α, and the other is a mouse line in which the p53 gene is deficient (p53- / -). Both of these mouse lines display accelerated accumulation of memory Tcell replacing naïve T-cells during ageing, accompanying progressively diminishing responsiveness to sheep red blood cell antigens for cytokine production. T-cells activate PKCα when they receive either an antigenic or stress stimulus. Repetitions of antigenic and stress stimuli that recurrently activate PKCα are probably mimicked by continuously elevated PKCα activity in the PKCα transgenic mice. Activated PKC-α probably counteracts the apoptosis-inducing signal, which prevents activation-induced cell death of T-cells and causes accumulation of memory T-cells as the descendants of activated T-cells that have survived. On the other hand, p53 is known to mediate the signaling for apoptosis induction that follows DNA damage due to oxidative stress. The apoptotic signal pathway fails to work well in p53- / - mice. During ageing of these mice, T-cells must encounter a number of antigenic and stress stimuli for activation, and activation of Tcells that is not followed by cell-death causes accumulation of memory T-cells. Results of experiments using the two transgenic mouse models for immunosenescence introduced here support the view that immunosenescence develops by chronic exposure to antigenic and stress stimuli, which is promoted by a defect in the mechanism for efficient elimination of activated T-cells.
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Methods for Quantitation and Clustering of Gene Expression Data
Authors: P. Toronen, G. Wong and E. CastrenThe analysis of gene expression has been revolutionized over recent years through the use of DNA arrays that can monitor the expression of thousands of genes. The field has great potential but requires a multidisciplinary approach integrating biochemistry with computer science, statistics, and engineering. Analysis of gene expression data has no golden rule, and the methodologies vary extensively between studies. Errors are inherent in data and should be estimated as part of the analysis.This article presents analytical methods applied to gene expression data. We describe the preprocessing steps and explain their need. The actual analysis of data concentrates on clustering of the gene expression data and how it can also be used in conjunction with other data sources like functional annotation. We also explain the functions of clustering algorithms.At the end we present a method for validation of clustering results that is based on probabilities of observed frequencies of functional classes within clusters. This method is able to integrate information from several functional classes and sort them out according to the statistical significance.
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Volumes & issues
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Volume 26 (2025)
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Volume 25 (2024)
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Volume 24 (2023)
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Volume 23 (2022)
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Volume 22 (2021)
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Volume 21 (2020)
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Volume 20 (2019)
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Volume 19 (2018)
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Volume 18 (2017)
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Volume 17 (2016)
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Volume 16 (2015)
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Volume 15 (2014)
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Volume 14 (2013)
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Volume 13 (2012)
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Volume 12 (2011)
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Volume 11 (2010)
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Volume 10 (2009)
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Volume 9 (2008)
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Volume 8 (2007)
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Volume 7 (2006)
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Volume 6 (2005)
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Volume 5 (2004)
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Volume 4 (2003)
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Volume 3 (2002)
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Volume 2 (2001)
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Volume 1 (2000)
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