28 research outputs found

    An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions

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    Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date

    WEclMon – A simple and robust camera-based system to monitor <i>Drosophila</i> eclosion under optogenetic manipulation and natural conditions

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    <div><p>Eclosion in flies and other insects is a circadian-gated behaviour under control of a central and a peripheral clock. It is not influenced by the motivational state of an animal, and thus presents an ideal paradigm to study the relation and signalling pathways between central and peripheral clocks, and downstream peptidergic regulatory systems. Little is known, however, about eclosion rhythmicity under natural conditions, and research into this direction is hampered by the physically closed design of current eclosion monitoring systems.</p><p>We describe a novel open eclosion monitoring system (WEclMon) that allows the puparia to come into direct contact with light, temperature and humidity. We demonstrate that the system can be used both in the laboratory and outdoors, and shows a performance similar to commercial closed funnel-type monitors. Data analysis is semi-automated based on a macro toolset for the open imaging software Fiji. Due to its open design, the WEclMon is also well suited for optogenetic experiments. A small screen to identify putative neuroendocrine signals mediating time from the central clock to initiate eclosion showed that optogenetic activation of ETH-, EH and myosuppressin neurons can induce precocious eclosion. Genetic ablation of myosuppressin-expressing neurons did, however, not affect eclosion rhythmicity.</p></div

    Characterization of intestinal inflammation and identification of related gene expression changes in mdr1a−/− mice

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    Multidrug resistance targeted mutation (mdr1a−/−) mice spontaneously develop intestinal inflammation. The aim of this study was to further characterize the intestinal inflammation in mdr1a−/− mice. Intestinal samples were collected to measure inflammation and gene expression changes over time. The first signs of inflammation occurred around 16 weeks of age and most mdr1a−/− mice developed inflammation between 16 and 27 weeks of age. The total histological injury score was the highest in the colon. The inflammatory lesions were transmural and discontinuous, revealing similarities to human inflammatory bowel diseases (IBD). Genes involved in inflammatory response pathways were up-regulated whereas genes involved in biotransformation and transport were down-regulated in colonic epithelial cell scrapings of inflamed mdra1−/− mice at 25 weeks of age compared to non-inflamed FVB mice. These results show overlap to human IBD and strengthen the use of this in vivo model to study human IBD. The anti-inflammatory regenerating islet-derived genes were expressed at a lower level during inflammation initiation in non-inflamed colonic epithelial cell scrapings of mdr1a−/− mice at 12 weeks of age. This result suggests that an insufficiently suppressed immune response could be crucial to the initiation and development of intestinal inflammation in mdr1a−/− mice
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