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      Metabolomics analysis of human acute graft-versus-host disease reveals changes in host and microbiota-derived metabolites

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          Abstract

          Despite improvement in clinical management, allogeneic hematopoietic stem cell transplantation (HSCT) is still hampered by high morbidity and mortality rates, mainly due to graft versus host disease (GvHD). Recently, it has been demonstrated that the allogeneic immune response might be influenced by external factors such as tissues microenvironment or host microbiota. Here we used high throughput metabolomics to analyze two cohorts of genotypically HLA-identical related recipient and donor pairs. Metabolomic profiles markedly differ between recipients and donors. At the onset of acute GvHD, in addition to host-derived metabolites, we identify significant variation in microbiota-derived metabolites, especially in aryl hydrocarbon receptor (AhR) ligands, bile acids and plasmalogens. Altogether, our findings support that the allogeneic immune response during acute GvHD might be influenced by bile acids and by the decreased production of AhR ligands by microbiota that could limit indoleamine 2,3-dioxygenase induction and influence allogeneic T cell reactivity.

          Abstract

          Graft versus host disease (GvHD) still hinders allogeneic hematopoietic stem cell transplantation. Here, the authors use metabolomics to analyze two cohorts of paired transplant recipients and donors, identifying significant differences in both host- and microbiota-derived metabolites.

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          Most cited references45

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          mixOmics: An R package for ‘omics feature selection and multiple data integration

          The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.
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            IDO expression by dendritic cells: tolerance and tryptophan catabolism.

            Indoleamine 2,3-dioxygenase (IDO) is an enzyme that degrades the essential amino acid tryptophan. The concept that cells expressing IDO can suppress T-cell responses and promote tolerance is a relatively new paradigm in immunology. Considerable evidence now supports this hypothesis, including studies of mammalian pregnancy, tumour resistance, chronic infections and autoimmune diseases. In this review, we summarize key recent developments and propose a unifying model for the role of IDO in tolerance induction.
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              Microbiota-Modulated Metabolites Shape the Intestinal Microenvironment by Regulating NLRP6 Inflammasome Signaling.

              Host-microbiome co-evolution drives homeostasis and disease susceptibility, yet regulatory principles governing the integrated intestinal host-commensal microenvironment remain obscure. While inflammasome signaling participates in these interactions, its activators and microbiome-modulating mechanisms are unknown. Here, we demonstrate that the microbiota-associated metabolites taurine, histamine, and spermine shape the host-microbiome interface by co-modulating NLRP6 inflammasome signaling, epithelial IL-18 secretion, and downstream anti-microbial peptide (AMP) profiles. Distortion of this balanced AMP landscape by inflammasome deficiency drives dysbiosis development. Upon fecal transfer, colitis-inducing microbiota hijacks this microenvironment-orchestrating machinery through metabolite-mediated inflammasome suppression, leading to distorted AMP balance favoring its preferential colonization. Restoration of the metabolite-inflammasome-AMP axis reinstates a normal microbiota and ameliorates colitis. Together, we identify microbial modulators of the NLRP6 inflammasome and highlight mechanisms by which microbiome-host interactions cooperatively drive microbial community stability through metabolite-mediated innate immune modulation. Therefore, targeted "postbiotic" metabolomic intervention may restore a normal microenvironment as treatment or prevention of dysbiosis-driven diseases.
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                Author and article information

                Contributors
                gerard.socie@aphp.fr
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 December 2019
                13 December 2019
                2019
                : 10
                : 5695
                Affiliations
                [1 ]ISNI 0000 0001 2300 6614, GRID grid.413328.f, Hematology Transplantation, , Saint Louis Hospital, ; 1 avenue Claude Vellefaux, 75010 Paris, France
                [2 ]ISNI 0000 0001 2171 2558, GRID grid.5842.b, Université de Paris, ; INSERM U976, 75010 Paris, France
                [3 ]Institut Pasteur, Immunoregulation Unit, Department of Immunology, 25 rue du Docteur Roux, 75015 Paris, France
                [4 ]Université de Paris, INSERM UMR-S1144, 75013 Paris, France
                [5 ]ISNI 0000 0001 2188 0914, GRID grid.10992.33, Université de Paris, Laboratoire de biomathématiques plateau iB2EA 7537–BioSTM, , Faculté de pharmacie, ; 75006 Paris, France
                [6 ]ISNI 0000 0001 2300 6614, GRID grid.413328.f, Service de Biostatistique et Information Médicale, , Hôpital Saint-Louis, ; AP-HP, 1 avenue Claude Vellefaux, 75010 Paris, France
                [7 ]GRID grid.429438.0, Metabolon, Inc., ; Morrisville, NC USA
                [8 ]Université de Paris, INSERM U1153, Epidemiology and Biostatistics Sorbonne Paris Cité Research Center (CRESS), ECSTRA Team, 75010 Paris, France
                Author information
                http://orcid.org/0000-0003-4553-3065
                http://orcid.org/0000-0001-8382-1493
                http://orcid.org/0000-0002-8009-5171
                http://orcid.org/0000-0003-1262-9204
                http://orcid.org/0000-0002-2114-7533
                Article
                13498
                10.1038/s41467-019-13498-3
                6910937
                31836702
                8ce0631c-8393-4ecf-b72b-a1718e34e14b
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 January 2019
                : 13 November 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001665, Agence Nationale de la Recherche (French National Research Agency);
                Award ID: ANR-PRTS 13002
                Award ID: ANR-PRTS 13002
                Award ID: ANR-PRTS13002
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100006364, Institut National Du Cancer (French National Cancer Institute);
                Award ID: INCa 2014-1-PL BIO-07-IP-1
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100006396, Alexion Pharmaceuticals (Alexion);
                Award ID: APALEX
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                metabolomics,transplant immunology,translational research
                Uncategorized
                metabolomics, transplant immunology, translational research

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