.2018 Oct;562(7728):589-594.

doi: 10.1038/s41586-018-0620-2. Epub 2018 Oct 24.

The human gut microbiome in early-onset type 1 diabetes from the TEDDY study

Tommi Vatanen¹, Eric A Franzosa^{2 3}, Randall Schwager³, Surya Tripathi², Timothy D Arthur², Kendra Vehik⁴, Åke Lernmark⁵, William A Hagopian⁶, Marian J Rewers⁷, Jin-Xiong She⁸, Jorma Toppari^{9 10}, Anette-G Ziegler^{11 12 13}, Beena Akolkar¹⁴, Jeffrey P Krischer⁴, Christopher J Stewart^{15 16}, Nadim J Ajami¹⁵, Joseph F Petrosino¹⁵, Dirk Gevers^{2 17}, Harri Lähdesmäki¹⁸, Hera Vlamakis², Curtis Huttenhower^{19 20}, Ramnik J Xavier^{21 22 23}

Affiliations

¹ Broad Institute of MIT and Harvard, Cambridge, MA, USA. vatanen@broadinstitute.org.
² Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³ Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
⁴ Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
⁵ Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital SUS, Malmo, Sweden.
⁶ Pacific Northwest Research Institute, Seattle, WA, USA.
⁷ Barbara Davis Center for Childhood Diabetes, University of Colorado, Aurora, CO, USA.
⁸ Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA, USA.
⁹ Department of Pediatrics, Turku University Hospital, Turku, Finland.
¹⁰ Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland.
¹¹ Institute of Diabetes Research, Helmholtz Zentrum München, Munich, Germany.
¹² Forschergruppe Diabetes, Technische Universität München, Klinikum Rechts der Isar, Munich, Germany.
¹³ Forschergruppe Diabetes e.V. at Helmholtz Zentrum München, Munich, Germany.
¹⁴ National Institute of Diabetes & Digestive & Kidney Diseases, Bethesda, MD, USA.
¹⁵ Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA.
¹⁶ Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK.
¹⁷ Janssen Human Microbiome Institute, Janssen Research and Development, Cambridge, MA, USA.
¹⁸ Department of Computer Science, Aalto University, Espoo, Finland.
¹⁹ Broad Institute of MIT and Harvard, Cambridge, MA, USA. chuttenh@hsph.harvard.edu.
²⁰ Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA. chuttenh@hsph.harvard.edu.
²¹ Broad Institute of MIT and Harvard, Cambridge, MA, USA. xavier@molbio.mgh.harvard.edu.
²² Gastrointestinal 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. xavier@molbio.mgh.harvard.edu.
²³ Center for Microbiome Informatics and Therapeutics, MIT, Cambridge, MA, USA. xavier@molbio.mgh.harvard.edu.

PMID:30356183
PMCID: PMC6296767
DOI: 10.1038/s41586-018-0620-2

The human gut microbiome in early-onset type 1 diabetes from the TEDDY study

Tommi Vatanen et al. Nature.2018 Oct.

.2018 Oct;562(7728):589-594.

doi: 10.1038/s41586-018-0620-2. Epub 2018 Oct 24.

Authors

Affiliations

¹ Broad Institute of MIT and Harvard, Cambridge, MA, USA. vatanen@broadinstitute.org.
² Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³ Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
⁴ Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
⁵ Department of Clinical Sciences, Lund University/CRC, Skåne University Hospital SUS, Malmo, Sweden.
⁶ Pacific Northwest Research Institute, Seattle, WA, USA.
⁷ Barbara Davis Center for Childhood Diabetes, University of Colorado, Aurora, CO, USA.
⁸ Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA, USA.
⁹ Department of Pediatrics, Turku University Hospital, Turku, Finland.
¹⁰ Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland.
¹¹ Institute of Diabetes Research, Helmholtz Zentrum München, Munich, Germany.
¹² Forschergruppe Diabetes, Technische Universität München, Klinikum Rechts der Isar, Munich, Germany.
¹³ Forschergruppe Diabetes e.V. at Helmholtz Zentrum München, Munich, Germany.
¹⁴ National Institute of Diabetes & Digestive & Kidney Diseases, Bethesda, MD, USA.
¹⁵ Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA.
¹⁶ Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK.
¹⁷ Janssen Human Microbiome Institute, Janssen Research and Development, Cambridge, MA, USA.
¹⁸ Department of Computer Science, Aalto University, Espoo, Finland.
¹⁹ Broad Institute of MIT and Harvard, Cambridge, MA, USA. chuttenh@hsph.harvard.edu.
²⁰ Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA. chuttenh@hsph.harvard.edu.
²¹ Broad Institute of MIT and Harvard, Cambridge, MA, USA. xavier@molbio.mgh.harvard.edu.
²² Gastrointestinal 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. xavier@molbio.mgh.harvard.edu.
²³ Center for Microbiome Informatics and Therapeutics, MIT, Cambridge, MA, USA. xavier@molbio.mgh.harvard.edu.

PMID:30356183
PMCID: PMC6296767
DOI: 10.1038/s41586-018-0620-2

Abstract

Type 1 diabetes (T1D) is an autoimmune disease that targets pancreatic islet beta cells and incorporates genetic and environmental factors¹, including complex genetic elements², patient exposures³ and the gut microbiome⁴. Viral infections⁵ and broader gut dysbioses⁶ have been identified as potential causes or contributing factors; however, human studies have not yet identified microbial compositional or functional triggers that are predictive of islet autoimmunity or T1D. Here we analyse 10,913 metagenomes in stool samples from 783 mostly white, non-Hispanic children. The samples were collected monthly from three months of age until the clinical end point (islet autoimmunity or T1D) in the The Environmental Determinants of Diabetes in the Young (TEDDY) study, to characterize the natural history of the early gut microbiome in connection to islet autoimmunity, T1D diagnosis, and other common early life events such as antibiotic treatments and probiotics. The microbiomes of control children contained more genes that were related to fermentation and the biosynthesis of short-chain fatty acids, but these were not consistently associated with particular taxa across geographically diverse clinical centres, suggesting that microbial factors associated with T1D are taxonomically diffuse but functionally more coherent. When we investigated the broader establishment and development of the infant microbiome, both taxonomic and functional profiles were dynamic and highly individualized, and dominated in the first year of life by one of three largely exclusive Bifidobacterium species (B. bifidum, B. breve or B. longum) or by the phylum Proteobacteria. In particular, the strain-specific carriage of genes for the utilization of human milk oligosaccharide within a subset of B. longum was present specifically in breast-fed infants. These analyses of TEDDY gut metagenomes provide, to our knowledge, the largest and most detailed longitudinal functional profile of the developing gut microbiome in relation to islet autoimmunity, T1D and other early childhood events. Together with existing evidence from human cohorts^7,8 and a T1D mouse model⁹, these data support the protective effects of short-chain fatty acids in early-onset human T1D.

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Conflict of interest statement

The authors declare no competing interests.

Figures

**Fig. 1. More than 10,000 longitudinal gut metagenomes from the TEDDY T1D cohort.**
We analysed 10,913 metagenomes collected longitudinally from 783 children (415 controls, 267 seroconverters, and 101 diagnosed with T1D) approximately monthly over the first five years of life.a, Subjects were recruited at six clinical centres (Finland, Sweden, Germany, Washington, Georgia and Colorado). Primary end points were seroconversion (defined as persistent confirmed IA) and T1D diagnosis. Additional metadata analysed for subjects and samples included the status of breastfeeding, birth mode, probiotics, antibiotics, formula feeding, and other dietary covariates.b, Overview of stool samples collected and microbiome development as summarized by Shannon’s alpha diversity and stratified by end point. Median number of samples per individualn = 12 (healthy controlsn = 10, seroconvertersn = 13, T1D casesn = 16).

**Fig. 2. The early gut microbiome is characterized by early heterogeneity ofBifidobacterium species and individualized accrual of taxa over time.**
a, Principal coordinate analysis (PCoA) ordination of microbial beta diversities (n = 10,913 samples), measured by Bray–Curtis dissimilarity. Arrows show the weighted averages of key taxonomic groups.b, Microbiota stability, measured by Bray–Curtis (BC) dissimilarity (n = 10,750 samples) in three-month time windows, over two-month increments, stratified into three groups: within subject, within clinical centre, and between clinical centres. Lines show median values per time window. Shaded area denotes the estimated 99% confidence interval. Gut microbial communities were highly individual.c, Influence of antibiotic (Abx) courses on microbial stability, measured by Bray–Curtis dissimilarity over consecutive stool samples (<50 days apart) from the same individual during the first three years of life, and stratified by whether antibiotics were given between the two samples (n = 654 observations with antibiotics,n = 6,734 observations without antibiotics). Curves show locally weighted scatterplot smoothing (LOESS) for the data per category. Shaded areas show permutation-based 95% confidence intervals for the fit.d, Decreases in the most commonBifidobacterium species in connection to oral antibiotic treatments. Fold change was measured between consecutive samples with an antibiotic course between them, given that the species in question was present in the first of the two samples. Sample size per species (n) indicates the number of sample pairs in which the species in question was present in the sample before the antibiotic treatment. Bars show bootstrapped mean log₂(fold change) (that is, decrease), and error bars denote s.d. (n = 1,000 bootstrap samples).

**Fig. 3. Consistent changes in enzymatic content of the gut microbiome in early life.**
We identified enzyme families (level-4 Enzyme Commission (EC) categories) that exhibited the most consistent within-subject changes in total community abundance between the ages of 3 months and 1 year. The top 20 most consistent increases or decreases are presented and stratified according to their top 15 contributing species. Heat map values reflect the mean contribution of each species to each enzyme over samples (n = 733 at 3 months; 675 at 1 year; and 382 at 2 years). Values reflect units of copies per million (CPM) normalized to total read depth (including unmapped reads and reads mapped to gene families lacking EC annotation). Rows (enzymes) and columns (species) are clustered according to Spearman correlation at 3 months; subsequent years are ordered according to clustering at 3 months.

**Fig. 4.Bifidobacterium longum strains are characterized by HMO gene content and stratified by breastfeeding status.**
Gene families involved in HMO utilization and showing contrasting presence inB. longum genomes during breastfeeding (n = 1,584 samples) compared to after weaning (n = 3,705 samples). Abundance heat map columns represent stool samples in which the relative abundance ofB. longum species was more than 10% (n = 5,289 samples). Rows and columns were ordered by hierarchical clustering using the complete linkage method. As in Fig. 3, values reflect units of CPM and were further divided by relative abundance ofB. longum to obtain quantifications that are comparable between samples. UniRef90 identifiers and gene names or families are indicated on the left.

**Extended Data Fig. 1. Heterogeneity in early taxonomic profiles.**
a–d, Relative abundances of taxonomic groups highlighted by weighted averages in Fig. 2a (arrows) shown separately (n = 10,913 samples).e,f, Average longitudinal abundance ofB. breve (e) andB. longum (f) per clinical centre (n = 10,194 samples). The curves show LOESS fits for the relative abundances, and shaded area shows 95% confidence interval for each fit, as implemented in geom_smooth function in ggplot2 R package.g–k, Growth curves of human infant isolates ofB. breve,*B. bifidum* andB. longum grown individually in low-nutrient medium (10% sBHI) supplemented with single carbon sources (glucose (g), galactose (h), fucose (i) and lactose (j)) or grown in 100% sBHI (k). As a negative control, growth curves of each strain grown in 10% BHI without additional sugar are shown in black for each condition. Data are representative of three independent experiments and are presented as the mean and s.d. of triplicate assessments.

**Extended Data Fig. 2. Stability and regional differences of taxonomic profiles.**
a, Stability of the microbiota, measured by the Jaccard index (n = 10,750 samples) in three-month time windows, over two-month increments, stratified into three groups: within subject, within clinical centre, and across clinical centres. Lines show the median per time window. Shaded areas show the 99% confidence interval estimated using binomial distribution. Compare to Fig. 2b, which shows the same analysis measured by Bray–Curtis dissimilarity.b–d, Average longitudinal abundance ofRuminococcus gnavus (b),*Lactobacillus rhamnosus* (c) andVeillonella parvula (d) per clinical centre (n = 10,194 samples). The curves show LOESS fit for the relative abundances, as above.

**Extended Data Fig. 3. Accrual of microbial alpha diversity.**
a, Shannon’s diversity of the taxonomic profiles of the gut microbial communities (n = 10,913 samples) with respect to the age at the sample collection. The curve shows the generalized additive model (GAM) fit for the data, and the shaded area shows the 95% confidence interval for each fit, as implemented in geom_smooth function in ggplot2 R package.b, Shannon’s diversity for the samples in the IA case–control cohort (n = 7,051) with respect to the time to the appearance of first autoantibody (seroconversion). The curves show LOESS fits for cases and controls separately, and the shaded area shows 95% confidence intervals for each fit.c, Shannon’s diversity for the samples in the T1D case–control cohort (n = 3,309) with respect to the time to T1D diagnosis. The curves and shaded areas are as inb.d, As inc, but only for data (n = 983 samples) for subjects in Finland. No difference between cases and controls.e, As inc, but only for data (n = 142 samples,n = 6 subjects) for subjects in Georgia, USA. Cases show a drop in alpha diversity before the diagnosis of T1D (linear mixed-effects model,P = 0.0033).

**Extended Data Fig. 4. Effects of antibiotics.**
a, Influence of antibiotic courses on microbial stability, stratified into six-month time windows (x axis). Stability was measured by Bray–Curtis dissimilarity over consecutive stool samples (<50 days apart) from the same individual between 3 and 29 months of age, and stratified by whether antibiotics were given between the two samples. For each notched box plot, the box denote the interquartile range (IQR), the horizontal line denotes the median, and the notch denotes the approximation for the 95% confidence interval (notch width = 1.58 × IQR/n^0.5, in whichn is the number of samples per box plot). Compare to Fig. 2c.b, Influence of antibiotic courses on microbial diversity. Notched box plots denote the increase (difference) in diversity between two consecutive stool samples (<50 days apart) stratified by antibiotic administration between the samples. Data show no difference between the groups (antibiotics versus no antibiotics).c, Influence of antibiotics courses on microbial diversity by antibiotic type; data fromb stratified into one-year time windows (x axis) and antibiotic types. No significant differences were detected between the antibiotic types.d,e, Influence of antibiotic courses on microbial stability by antibiotic type; data from Fig. 2c and Extended Data Fig. 3a stratified by antibiotic type.d, LOESS fit for the relative abundances (shaded area shows 95% confidence interval for each fit, as implemented in geom_smooth function in ggplot2 R package).e, Notched box plots (as ina andb) for the data per antibiotic type. No significant differences were detected between the antibiotic types. No antibiotics,n = 7,130; amoxicillin,n = 268; penicillin,n = 90; cephalosporin,n = 51; macrolide,n = 60; other,n = 101.f, Decreases in relative abundance of bacteria over antibiotic courses. Bacteria for which the bootstrapped 95% confidence interval of the fold change does not overlap zero are shown. Fold change was measured between consecutive samples with an antibiotic course between them, given that the species in question was present in the first of the two samples. Sample size per species (n) indicate the number of sample pairs in which the species in question was present in the sample preceding the antibiotic treatment. Bars denote bootstrapped mean log₂(fold change) (that is, decrease), and error bars denote s.d. (n = 1,000 bootstrap samples).

**Extended Data Fig. 5. Dynamics of species-specific microbial functional potential during early gut development.**
a,b, Stability of microbial pathways (n = 10,580 samples) measured by Bray–Curtis dissimilarity (a) and the Jaccard index (b) and stratified into three groups: within subject, within clinical centre, and across clinical centres. Although the baseline level of functional similarity is significantly greater than that of taxa (see Fig. 2b), functional states and development trajectories also both retain a level of personalization. The stability of the functional profiles was evaluated in three-month time windows, over two-month increments. Lines show the median per time window, and shaded area denotes the 99% confidence interval estimated using binomial distribution.c,d, Proportion of metagenomic gene abundance with functional annotation through Gene Ontology (c) and MetaCyc (d) databases. The metagenomic reads were divided into the following categories: reads that could be mapped to genes with functional assignment in the database in question (annotated), and reads with no annotation but alignment to species pangenomes or UniProt proteins (unannotated). The proportion of the unknown genes (unmapped) was estimated using the number of reads with unknown origin.e, The proportion of unmapped reads, reflecting the relative abundances of reads not mappable to any microbial pangenomes in the available reference set or to UniProt. An increasing trend of unmapped reads with respect to the age at sample collection continued through approximately two years of age.f, The proportion of reads with confident functional annotation in MetaCyc within the genes that mapped to species pangenomes or UniProt proteins. The data again showed an increasing longitudinal trend, implicating a deficit of functional and biochemical annotations within microorganisms that are abundant during the first year of life.

**Extended Data Fig. 6. Differences between cases and controls.**
a, The gut microbiome functional (left) and taxonomic (right) profiles were classified between cases and controls using leave-one-out cross-validation (n = 3,366 samples), in which one case–control pair was held-out in turn. Data show error rates for classifying these held-out samples per fold (a data point per fold,n = 100 folds). This suggests weak but better-than-random classification between cases and controls. Notched box plots are as in Extended Data Fig. 4.b, Average longitudinal abundance ofRuminococcus gnavus in Finland (n = 2,630 samples) stratified by the number of observed persistent autoantibodies (AABs); no autoantibodies (that is, healthy control), a single autoantibody, or multiple (two or more) autoantibodies.c, Average longitudinal abundance ofLactobacillus rhamnosus in IA cases and controls (n = 7,017 samples).L. rhamnosus is more abundant in controls (q = 0.055). The curves inb andc show LOESS fit per group, and shaded areas show 95% confidence interval for each fit, as implemented in geom_smooth function in ggplot2 R package.d, Abundance (left) and prevalence (right) ofLactobacillus reuteri andL. rhamnosus in the first stool sample of each individual (collected at approximately three months of age) in association with early probiotic supplementation. ‘No probiotic’ indicates no probiotics given before the first stool sample (n = 583); ‘later probiotic’ refers to probiotics given later than the first four weeks but before the first stool sample (n = 45); ‘early probiotic’ refers to probiotics given during the first four weeks of life (n = 84). Numbers (n) per clinical centre are given in Extended Data Table 2.L. reuteri andL. rhamnosus were more abundant and prevalent in groups with probiotics supplementation. Visual jitter was added to make data equal to zero distinguishable, and boxes denote the IQR, when applicable. The shownP values were obtained by applying Fisher’s exact test (two-sided) to presence or absence count data (counting samples in which the species were present).

**Extended Data Fig. 7. Contrasting HMO utilization genes inB. pseudocatenulatum.**
The gene families involved in HMO utilization and that show contrasting presence inB. pseudocatenulatum genomes during breastfeeding (n = 321 samples) compared to after weaning (n = 1,004 samples). Columns represent stool samples in which the relative abundance ofB. pseudocatenulatum species was greater than 10% (n = 1,325 samples). Rows and columns were ordered by hierarchical clustering using complete linkage method. Compare to Fig. 4, which shows similar data forB. longum. UniRef90 identifiers and gene names or families are indicated on the left.

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References

1. Katsarou A, et al. Type 1 diabetes mellitus. Nat. Rev. Dis. Primers. 2017;3:17016. doi: 10.1038/nrdp.2017.16. - DOI - PubMed
1. Pociot F, Lernmark Å. Genetic risk factors for type 1 diabetes. Lancet. 2016;387:2331–2339. doi: 10.1016/S0140-6736(16)30582-7. - DOI - PubMed
1. Rewers M, Ludvigsson J. Environmental risk factors for type 1 diabetes. Lancet. 2016;387:2340–2348. doi: 10.1016/S0140-6736(16)30507-4. - DOI - PMC - PubMed
1. Knip M, Siljander H. The role of the intestinal microbiota in type 1 diabetes mellitus. Nat. Rev. Endocrinol. 2016;12:154–167. doi: 10.1038/nrendo.2015.218. - DOI - PubMed
1. Hober D, Sauter P. Pathogenesis of type 1 diabetes mellitus: interplay between enterovirus and host. Nat. Rev. Endocrinol. 2010;6:279–289. doi: 10.1038/nrendo.2010.27. - DOI - PubMed

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The human gut microbiome in early-onset type 1 diabetes from the TEDDY study

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The human gut microbiome in early-onset type 1 diabetes from the TEDDY study

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