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  • Articles  (2)
  • Oxford University Press  (2)
  • Medicine  (2)
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  • 1
    Publication Date: 2014-10-22
    Description: Golgi fragmentation is an early hallmark of many neurodegenerative diseases but its pathophysiological relevance and molecular mechanisms are unclear. We here demonstrate severe and progressive Golgi fragmentation in motor neurons of progressive motor neuronopathy (pmn) mice due to loss of the Golgi-localized tubulin-binding cofactor E (TBCE). Loss of TBCE in mutant pmn and TBCE-depleted motor neuron cultures causes defects in Golgi-derived microtubules, as expected, but surprisingly also reduced levels of COPI subunits, decreased recruitment of tethering factors p115/GM130 and impaired Golgi SNARE-mediated vesicle fusion. Conversely, ARF1, which stimulates COPI vesicle formation, enhances the recruitment of TBCE to the Golgi, increases polymerization of Golgi-derived microtubules and rescues TBCE-linked Golgi fragmentation. These data indicate an ARF1/TBCE-mediated cross-talk that coordinates COPI formation and tubulin polymerization at the Golgi. We conclude that interruption of this cross-talk causes Golgi fragmentation in pmn mice and hypothesize that similar mechanisms operate in human amyotrophic lateral sclerosis and spinal muscular atrophy.
    Print ISSN: 0964-6906
    Electronic ISSN: 1460-2083
    Topics: Biology , Medicine
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  • 2
    Publication Date: 2014-04-11
    Description: Motivation: Histone modifications are a key epigenetic mechanism to activate or repress the transcription of genes. Datasets of matched transcription data and histone modification data obtained by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatics approach to detect genes that show different transcript abundances between two conditions putatively caused by alterations in histone modification. Results: We introduce a correlation measure for integrative analysis of ChIP-seq and gene transcription data measured by RNA sequencing or microarrays and demonstrate that a proper normalization of ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different types of distributions to further study the distribution of the correlation measure. The implicit classification of the mixture models is used to detect genes with differences between two conditions in both gene transcription and histone modification. The method is applied to different datasets, and its superiority to a naive separate analysis of both data types is demonstrated. Availability and implementation: R/Bioconductor package epigenomix. Contact: h.klein@uni-muenster.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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