77,961 research outputs found

    Differential variability analysis of gene expression and its application to human diseases

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    Motivation: Current microarray analyses focus on identifying sets of genes that are differentially expressed (DE) or differentially coexpressed (DC) in different biological states (e.g. diseased versus non-diseased). We observed that in many human diseases, some genes have a significantincrease or decrease in expression variability (variance). Asthese observed changes in expression variability may be caused by alteration of the underlying expression dynamics, such differential variability (DV) patterns are also biologically interesting

    Sparse multi-view matrix factorisation: a multivariate approach to multiple tissue comparisons

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    Gene expression levels in a population vary extensively across tissues. Such heterogeneity is caused by genetic variability and environmental factors, and is expected to be linked to disease development. The abundance of experimental data now enables the identification of features of gene expression profiles that are shared across tissues, and those that are tissue-specific. While most current research is concerned with characterising differential expression by comparing mean expression profiles across tissues, it is also believed that a significant difference in a gene expression's variance across tissues may also be associated to molecular mechanisms that are important for tissue development and function. We propose a sparse multi-view matrix factorisation (sMVMF) algorithm to jointly analyse gene expression measurements in multiple tissues, where each tissue provides a different "view" of the underlying organism. The proposed methodology can be interpreted as an extension of principal component analysis in that it provides the means to decompose the total sample variance in each tissue into the sum of two components: one capturing the variance that is shared across tissues, and one isolating the tissue-specific variances. sMVMF has been used to jointly model mRNA expression profiles in three tissues - adipose, skin and LCL - which are available for a large and well-phenotyped twins cohort, TwinsUK. Using sMVMF, we are able to prioritise genes based on whether their variation patterns are specific to each tissue. Furthermore, using DNA methylation profiles available, we provide supporting evidence that adipose-specific gene expression patterns may be driven by epigenetic effects.Comment: in Bioinformatics 201

    Systems biology in animal sciences

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    Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed ‘omics’ technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A ‘system’ approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with ‘system approaches’ in animal sciences, providing exciting opportunities to predict and modulate animal traits

    Current epigenetic aspects the clinical kidney researcher should embrace

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    Chronic kidney disease (CKD), affecting 10-12% of the world's adult population, is associated with a considerably elevated risk of serious comorbidities, in particular, premature vascular disease and death. Although a wide spectrum of causative factors has been identified and/or suggested, there is still a large gap of knowledge regarding the underlying mechanisms and the complexity of the CKD phenotype. Epigenetic factors, which calibrate the genetic code, are emerging as important players in the CKD-associated pathophysiology. In this article, we review some of the current knowledge on epigenetic modifications and aspects on their role in the perturbed uraemic milieu, as well as the prospect of applying epigenotype-based diagnostics and preventive and therapeutic tools of clinical relevance to CKD patients. The practical realization of such a paradigm will require that researchers apply a holistic approach, including the full spectrum of the epigenetic landscape as well as the variability between and within tissues in the uraemic milieu

    Potentially Diagnostic Electron Paramagnetic Resonance Spectra Elucidate the Underlying Mechanism of Mitochondrial Dysfunction in the Deoxyguanosine Kinase Deficient Rat Model of a Genetic Mitochondrial DNA Depletion Syndrome

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    A novel rat model for a well-characterized human mitochondrial disease, mitochondrial DNA depletion syndrome with associated deoxyguanosine kinase (DGUOK) deficiency, is described. The rat model recapitulates the pathologic and biochemical signatures of the human disease. The application of electron paramagnetic (spin) resonance (EPR) spectroscopy to the identification and characterization of respiratory chain abnormalities in the mitochondria from freshly frozen tissue of the mitochondrial disease model rat is introduced. EPR is shown to be a sensitive technique for detecting mitochondrial functional abnormalities in situ and, here, is particularly useful in characterizing the redox state changes and oxidative stress that can result from depressed expression and/or diminished specific activity of the distinct respiratory chain complexes. As EPR requires no sample preparation or non-physiological reagents, it provides information on the status of the mitochondrion as it was in the functioning state. On its own, this information is of use in identifying respiratory chain dysfunction; in conjunction with other techniques, the information from EPR shows how the respiratory chain is affected at the molecular level by the dysfunction. It is proposed that EPR has a role in mechanistic pathophysiological studies of mitochondrial disease and could be used to study the impact of new treatment modalities or as an additional diagnostic tool
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