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The Human Gut Microbiome in Early-Onset Type 1 Diabetes from the TEDDY Study Tommi Vatanen1*, Eric A

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The Human Gut Microbiome in Early-Onset Type 1 Diabetes from the TEDDY Study Tommi Vatanen1*, Eric A OPEN LETTER https://doi.org/10.1038/s41586-018-0620-2 The human gut microbiome in early-onset type 1 diabetes from the TEDDY study Tommi Vatanen1*, Eric A. Franzosa1,2, Randall Schwager2, Surya Tripathi1, Timothy D. Arthur1, Kendra Vehik3, Åke Lernmark4, William A. Hagopian5, Marian J. Rewers6, Jin-Xiong She7, Jorma Toppari8,9, Anette-G. Ziegler10,11,12, Beena Akolkar13, Jeffrey P. Krischer3, Christopher J. Stewart14,15, Nadim J. Ajami14, Joseph F. Petrosino14, Dirk Gevers1,19, Harri Lähdesmäki16, Hera Vlamakis1, Curtis Huttenhower1,2,20* & Ramnik J. Xavier1,17,18,20* Type 1 diabetes (T1D) is an autoimmune disease that targets species, and deficiency of bacteria that produce short-chain fatty pancreatic islet beta cells and incorporates genetic and acids (SCFAs)7,8 in cases of T1D or islet autoimmunity (IA)8,11,15,18. environmental factors1, including complex genetic elements2, Corroborating these findings, decreased levels of SCFA-producing patient exposures3 and the gut microbiome4. Viral infections5 bacteria were found in adults with type 2 diabetes (T2D)19. In addi- and broader gut dysbioses6 have been identified as potential tion, increased intestinal permeability14 and decreased microbial diver- causes or contributing factors; however, human studies have not sity12 after IA but before T1D diagnosis have been reported. Studies yet identified microbial compositional or functional triggers that using the nonobese diabetic (NOD) mouse model have determined are predictive of islet autoimmunity or T1D. Here we analyse immune mechanisms that mediate the protective effects of SCFAs9 10,913 metagenomes in stool samples from 783 mostly white, non- and the microbiome-linked sex bias in autoimmunity20. NOD mice Hispanic children. The samples were collected monthly from three fed specialized diets resulting in high bacterial release of the SCFAs months of age until the clinical end point (islet autoimmunity or acetate and butyrate were almost completely protected from T1D9. A T1D) in the The Environmental Determinants of Diabetes in the study in a streptozotocin-induced T1D mouse model demonstrated Young (TEDDY) study, to characterize the natural history of the that bacterial products recognized in pancreatic lymph nodes contribute early gut microbiome in connection to islet autoimmunity, T1D to pathogenesis21. diagnosis, and other common early life events such as antibiotic Even in the absence of immune perturbation, the first few weeks, treatments and probiotics. The microbiomes of control children months and years of life represent a unique human microbial environ- contained more genes that were related to fermentation and ment that has only recently been detailed22,23. Infants have a markedly the biosynthesis of short-chain fatty acids, but these were not different gut microbial profile from adults, characterized by a dis- consistently associated with particular taxa across geographically tinct taxonomic profile, greater proportion of aerobic energy harvest diverse clinical centres, suggesting that microbial factors associated metabo lism, and more extreme dynamic change24. These differences with T1D are taxonomically diffuse but functionally more coherent. gradually fade over the first few years of life, particularly in response to When we investigated the broader establishment and development the introduction of solid food, and individual microbial developmental of the infant microbiome, both taxonomic and functional profiles trajectories are influenced by environment, delivery mode, breast (versus were dynamic and highly individualized, and dominated in the formula) feeding, and antibiotics25–27. Most studies that address the first year of life by one of three largely exclusive Bifidobacterium development of the gut microbiome, both generally and in associa- species (B. bifidum, B. breve or B. longum) or by the phylum tion with T1D, have used gene analysis of 16S rRNA, which leaves Proteobacteria. In particular, the strain-specific carriage of genes open the question of functional and strain-specific differences that are for the utilization of human milk oligosaccharide within a subset not easily detected by this technology that might contribute to disease of B. longum was present specifically in breast-fed infants. These pathogenesis12. analyses of TEDDY gut metagenomes provide, to our knowledge, Bridging this gap is one goal of the The Environmental Determinants the largest and most detailed longitudinal functional profile of the of Diabetes in the Young (TEDDY) study, a prospective study that developing gut microbiome in relation to islet autoimmunity, T1D aims to identify environmental causes of T1D28. It includes six clinical and other early childhood events. Together with existing evidence research centres in the United States (Colorado, Georgia/Florida and from human cohorts7,8 and a T1D mouse model9, these data support Washington) and Europe (Finland, Germany and Sweden), which the protective effects of short-chain fatty acids in early-onset human together have recruited several thousand newborns with a genetic T1D. predisposition for T1D or first-degree relative(s) with T1D. This has Recent literature has linked several facets of gut health with the enabled the TEDDY study to collect a range of biospecimens, including onset of T1D in humans and rodent models4,6,10. Altered intestinal monthly stool samples starting at three months of age, coupled with microbiota in connection to T1D has been reported in Finnish7,8,11,12, extensive clinical and personal data such as diet, illnesses, medications German13, Italian14, Mexican15, American (Colorado)16 and Turkish17 and other life experiences. To characterize microbial, environmental, children. Common findings include increased numbers of Bacteroides genetic, immunological and additional contributors to the development 1Broad Institute of MIT and Harvard, Cambridge, MA, USA. 2Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA. 3Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA. 4Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital SUS, Malmo, Sweden. 5Pacific Northwest Research Institute, Seattle, WA, USA. 6Barbara Davis Center for Childhood Diabetes, University of Colorado, Aurora, CO, USA. 7Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA, USA. 8Department of Pediatrics, Turku University Hospital, Turku, Finland. 9Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland. 10Institute of Diabetes Research, Helmholtz Zentrum München, Munich, Germany. 11Forschergruppe Diabetes, Technische Universität München, Klinikum Rechts der Isar, Munich, Germany. 12Forschergruppe Diabetes e.V. at Helmholtz Zentrum München, Munich, Germany. 13National Institute of Diabetes & Digestive & Kidney Diseases, Bethesda, MD, USA. 14Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA. 15Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK. 16Department of Computer Science, Aalto University, Espoo, Finland. 17Gastrointestinal Unit, Center for the Study of Inflammatory Bowel Disease, and Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. 18Center for Microbiome Informatics and Therapeutics, MIT, Cambridge, MA, USA. 19Present address: Janssen Human Microbiome Institute, Janssen Research and Development, Cambridge, MA, USA. 20These authors jointly supervised this work: Curtis Huttenhower, Ramnik J. Xavier. *e-mail: [email protected]; [email protected]; [email protected] 25 OCTOBER 2018 | VOL 562 | NATURE | 589 © 2018 Springer Nature Limited. All rights reserved. RESEARCH LETTER a b Seroconverted; Healthy controls (n = 415) T1D not diagnosed (n = 267) Healthy sample 5,065 samples Last sample before seroconversion 4,043 samples Last sample before T1D diagnosis Seroconverted; T1D not diagnosed Age ~3 months Seroconversion Subject Paired control ~1 month T1D diagnosed Seroconversion T1D Subject Subjects Paired control Metadata and clinical covariates Subjects Basic information Sex Gestational age Mode of delivery HLA haplotypes Breastfeeding Gluten Dietary information T1D diagnosed (n = 101) (introduction/cessation) Baby formula Meat 1,805 samples Cow’s milk Fruits Cereals Vegetables ShannonShannon diversitydiversity Antibiotics Amoxicillin Macrolide Penicillin Others 0 1 2233 Cephalosporins Subjects Probiotics T1D-associated Status (increased/normal) autoantibodies Persistence of AABs (IAA, GADA, IA2A) 012345 012345 Age (years) Age (years) Fig. 1 | More than 10,000 longitudinal gut metagenomes from the Additional metadata analysed for subjects and samples included the status TEDDY T1D cohort. We analysed 10,913 metagenomes collected of breastfeeding, birth mode, probiotics, antibiotics, formula feeding, longitudinally from 783 children (415 controls, 267 seroconverters, and and other dietary covariates. b, Overview of stool samples collected and 101 diagnosed with T1D) approximately monthly over the first five years microbiome development as summarized by Shannon’s alpha diversity and of life. a, Subjects were recruited at six clinical centres (Finland, Sweden, stratified by end point. Median number of samples per individual n = 12 Germany,
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