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      Focused Specificity of Intestinal Th17 Cells towards Commensal Bacterial Antigens

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          Abstract

          T-helper-17 (Th17) cells have critical roles in mucosal defense and in autoimmune disease pathogenesis 1- 3 . They are most abundant in the small intestine lamina propria (SILP), where their presence requires colonization of mice with microbiota 4- 7 . Segmented Filamentous Bacteria ( SFB) are sufficient to induce Th17 cells and to promote Th17-dependent autoimmune disease in animal models 8- 14 . However, the specificity of Th17 cells, the mechanism of their induction by distinct bacteria, and the means by which they foster tissue-specific inflammation remain unknown. Here we show that the T cell receptor (TCR) repertoire of intestinal Th17 cells in SFB-colonized mice has minimal overlap with that of other intestinal CD4 + T cells and that most Th17 cells, but not other T cells, recognize antigens encoded by SFB. T cells with antigen receptors specific for SFB-encoded peptides differentiated into RORγt-expressing Th17 cells, even if SFB-colonized mice also harbored a strong Th1 cell inducer, Listeria monocytogenes, in their intestine. The match of T cell effector function with antigen specificity is thus determined by the type of bacteria that produce the antigen. These findings have significant implications for understanding how commensal microbiota contribute to organ-specific autoimmunity and for developing novel mucosal vaccines.

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          Most cited references22

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          Prediction of protein subcellular localization.

          Because the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets-one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of subcellular localization.
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            Gut-residing segmented filamentous bacteria drive autoimmune arthritis via T helper 17 cells.

            Commensal microbes can have a substantial impact on autoimmune disorders, but the underlying molecular and cellular mechanisms remain largely unexplored. We report that autoimmune arthritis was strongly attenuated in the K/BxN mouse model under germ-free (GF) conditions, accompanied by reductions in serum autoantibody titers, splenic autoantibody-secreting cells, germinal centers, and the splenic T helper 17 (Th17) cell population. Neutralization of interleukin-17 prevented arthritis development in specific-pathogen-free K/BxN mice resulting from a direct effect of this cytokine on B cells to inhibit germinal center formation. The systemic deficiencies of the GF animals reflected a loss of Th17 cells from the small intestinal lamina propria. Introduction of a single gut-residing species, segmented filamentous bacteria, into GF animals reinstated the lamina propria Th17 cell compartment and production of autoantibodies, and arthritis rapidly ensued. Thus, a single commensal microbe, via its ability to promote a specific Th cell subset, can drive an autoimmune disease. Copyright 2010 Elsevier Inc. All rights reserved.
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              Proinflammatory T-cell responses to gut microbiota promote experimental autoimmune encephalomyelitis.

              Although the effects of commensal bacteria on intestinal immune development seem to be profound, it remains speculative whether the gut microbiota influences extraintestinal biological functions. Multiple sclerosis (MS) is a devastating autoimmune disease leading to progressive deterioration of neurological function. Although the cause of MS is unknown, microorganisms seem to be important for the onset and/or progression of disease. However, it is unclear how microbial colonization, either symbiotic or infectious, affects autoimmunity. Herein, we investigate a role for the microbiota during the induction of experimental autoimmune encephalomyelitis (EAE), an animal model for MS. Mice maintained under germ-free conditions develop significantly attenuated EAE compared with conventionally colonized mice. Germ-free animals, induced for EAE, produce lower levels of the proinflammatory cytokines IFN-γ and IL-17A in both the intestine and spinal cord but display a reciprocal increase in CD4(+)CD25(+)Foxp3(+) regulatory T cells (Tregs). Mechanistically, we show that gut dendritic cells from germ-free animals are reduced in the ability to stimulate proinflammatory T cell responses. Intestinal colonization with segmented filamentous bacteria (SFB) is known to promote IL-17 production in the gut; here, we show that SFBs also induced IL-17A-producing CD4(+) T cells (Th17) in the CNS. Remarkably, germ-free animals harboring SFBs alone developed EAE, showing that gut bacteria can affect neurologic inflammation. These findings reveal that the intestinal microbiota profoundly impacts the balance between pro- and antiinflammatory immune responses during EAE and suggest that modulation of gut bacteria may provide therapeutic targets for extraintestinal inflammatory diseases such as MS.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                15 July 2014
                13 April 2014
                5 June 2014
                05 December 2014
                : 510
                : 7503
                : 152-156
                Affiliations
                [1 ]The Kimmel Center for Biology and Medicine of the Skirball Institute, New York University School of Medicine, New York, New York, 10016, USA
                [2 ]Department of Microbiology, Center for Immunology, University of Minnesota Medical School, Minneapolis, Minnesota, 55455, USA
                [3 ]Department of Microbiology, New York University School of Medicine, New York, New York, 10016, USA
                [4 ]Howard Hughes Medical Institute, New York University School of Medicine, New York, New York, 10016, USA
                Author notes
                [* ]To whom correspondence should be addressed. dan.littman@ 123456med.nyu.edu
                Article
                NIHMS579568
                10.1038/nature13279
                4128479
                24739972
                c3b80141-17be-43f3-afff-d1d2211841b5
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