Trisomy 21 activates the kynurenine pathway via increased dosage of interferon receptors

Altres ajuts: This work has also been supported by a "Marató TV3" grant (20141210 to J.F. and 044412 to R.B.).Trisomy 21 (T21) causes Down syndrome (DS), affecting immune and neurological function by ill-defined mechanisms. Here we report a large metabolomics study of plasma and cerebrospinal fluid, showing in independent cohorts that people with DS produce elevated levels of kynurenine and quinolinic acid, two tryptophan catabolites with potent immunosuppressive and neurotoxic properties, respectively. Immune cells of people with DS overexpress IDO1, the rate-limiting enzyme in the kynurenine pathway (KP) and a known interferon (IFN)-stimulated gene. Furthermore, the levels of IFN-inducible cytokines positively correlate with KP dysregulation. Using metabolic tracing assays, we show that overexpression of IFN receptors encoded on chromosome 21 contribute to enhanced IFN stimulation, thereby causing IDO1 overexpression and kynurenine overproduction in cells with T21. Finally, a mouse model of DS carrying triplication of IFN receptors exhibits KP dysregulation. Together, our results reveal a mechanism by which T21 could drive immunosuppression and neurotoxicity in DS

A boosting approach to multiview classification with cooperation

International audienceNowadays in numerous fields such as bioinformatics or multimedia, data may be described using many different sets of features (or views) which carry either global or local information. Many learning tasks make use of these competitive views in order to improve overall predictive power of classifiers through fusion-based methods. Usually, these approaches rely on a weighted combination of classifiers (or selected descriptions), where classifiers are learnt independently the ones from the others. One drawback of these methods is that the classifier learnt on one view does not communicate its lack to the other views. In other words, learning algorithms do not cooperate although they are trained on the same objects. This paper deals with a novel approach to integrate multiview information within an iterative learning scheme, where the classifier learnt on one view is allowed to somehow communicate its performances to the other views. The proposed algorithm, named Mumbo, is based on boosting. Within the boosting scheme, Mumbo maintains one distribution of examples on each view, and at each round, it learns one weak classifier on each view. Within a view, the distribution of examples evolves both with the ability of the dedicated classifier to deal with examples of the corresponding features space, and with the ability of classifiers in other views to process the same examples within their own description spaces. Hence, the principle is to slightly remove the hard examples from the learning space of one view, while their weights get higher in the other views. This way, we expect that examples are urged to be processed by the most appropriate views, when possible. At the end of the iterative learning process, a final classifier is computed by a weighted combination of selected weak classifiers. Such an approach is merely useful when some examples detected as outliers in a view -- for instance because of noise -- are quite probabilisticaly regular hence informative within some other view. This paper provides the Mumbo algorithm in a multiclass and multiview setting, based on recent advances in theoretical boosting. The boosting properties of Mumbo are proven, as well as a some results on its generalization capabilities. Several experimental results are reported which point out that complementary views may actually cooperate under some assumptions