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Prospective virome analyses in young children at increased genetic risk for type 1 diabetes

Nature Medicine volume 25, pages 1865–1872 (2019)Cite this article

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Abstract

Viruses are implicated in autoimmune destruction of pancreatic islet β cells, which results in insulin deficiency and type 1 diabetes (T1D)1,2,3,4. Certain enteroviruses can infect β cells in vitro5, have been detected in the pancreatic islets of patients with T1D6 and have shown an association with T1D in meta-analyses4. However, establishing consistency in findings across studies has proven difficult. Obstacles to convincingly linking RNA viruses to islet autoimmunity may be attributed to rapid viral mutation rates, the cyclical periodicity of viruses7 and the selection of variants with altered pathogenicity and ability to spread in populations. β cells strongly express cell-surface coxsackie and adenovirus receptor (CXADR) genes, which can facilitate enterovirus infection8. Studies of human pancreata and cultured islets have shown significant variation in enteroviral virulence to β cells between serotypes and within the same serotype9,10. In this large-scale study of known eukaryotic DNA and RNA viruses in stools from children, we evaluated fecally shed viruses in relation to islet autoimmunity and T1D. This study showed that prolonged enterovirus B rather than independent, short-duration enterovirus B infections may be involved in the development of islet autoimmunity, but not T1D, in some young children. Furthermore, we found that fewer early-life human mastadenovirus C infections, as well as CXADR rs6517774, independently correlated with islet autoimmunity.

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Fig. 1: Stool virome composition up to 36 months of age, and common human viruses related to islet autoimmunity.
Fig. 2: Percentages of children positive for a specific virus between the ages of 3 and 6 months, and percentages of children with consecutive positive samples before islet autoimmunity development, by clinical site.
Fig. 3: Risk of islet autoimmunity, as predicted by EV-B infection frequency.
Fig. 4: Multivariable conditional logistic regression of EV-B, HAdV-C and HAdV-F on islet autoimmunity status.

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Data availability

TEDDY virome sequencing data that support the findings of this study have been deposited in the NCBI database of Genotypes and Phenotypes (dbGaP) with the primary accession code phs001442, in accordance with the dbGaP controlled-access authorization process. Clinical metadata and virome results data analyzed for the current study will be made available in the NIDDK Central Repository at https://www.niddkrepository.org/studies/teddy.

Code availability

VirMAP was used to generate the virome data and has been deposited in GitHub (https://github.com/cmmr/virmap). All of the software code and dependencies are listed on the GitHub site. SAS 9.4 (SAS Institute) was used for the statistical analysis, and GraphPad Prism 8.0 was used to create the figures.

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Acknowledgements

The TEDDY Study is funded by U01 DK63829, U01 DK63861, U01 DK63821, U01 DK63865, U01 DK63863, U01 DK63836, U01 DK63790, UC4 DK63829, UC4 DK63861, UC4 DK63821, UC4 DK63865, UC4 DK63863, UC4 DK63836, UC4 DK95300, UC4 DK100238, UC4 DK106955, UC4 DK112243, UC4 DK117483 and contract number HHSN267200700014C from the NIDDK, National Institute of Allergy and Infectious Diseases, National Institute of Child Health and Human Development, National Institute of Environmental Health Sciences, Centers for Disease Control and Prevention and JDRF. This work was supported in part by the NIH/NCATS Clinical and Translational Science Awards to the University of Florida (UL1 TR000064) and the University of Colorado (UL1 TR001082).

Author information

Author notes

  1. A list of members and affiliations appears in the Supplementary Note.

Authors and Affiliations

  1. Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA

    Kendra Vehik, Kristian F. Lynch & Jeffrey P. Krischer

  2. Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA

    Matthew C. Wong, Xiangjun Tian, Matthew C. Ross, Nadim J. Ajami, Joseph F. Petrosino & Richard E. Lloyd

  3. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

    Richard A. Gibbs

  4. Barbara Davis Center for Diabetes, University of Colorado, Aurora, CO, USA

    Marian Rewers

  5. Department of Pediatrics, Turku University Hospital, Turku, Finland

    Jorma Toppari

  6. Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland

    Jorma Toppari

  7. Institute of Diabetes Research, Helmholtz Zentrum München, Munich, Germany

    Anette G. Ziegler

  8. Forschergruppe Diabetes, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany

    Anette G. Ziegler

  9. Forschergruppe Diabetes e.V, Munich, Germany

    Anette G. Ziegler

  10. Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA, USA

    Jin-Xiong She

  11. Department of Clinical Sciences, Lund University/CRC, Skane University Hospital, Malmö, Sweden

    Ake Lernmark

  12. National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA

    Beena Akolkar

  13. Pacific Northwest Research Institute, Seattle, WA, USA

    William A. Hagopian

  14. Department of Pediatrics, University of Florida Diabetes Institute, Gainesville, FL, USA

    Desmond A. Schatz

  15. Department of Virology, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland

    Heikki Hyöty

  16. Fimlab Laboratories, Pirkanmaa Hospital District, Tampere, Finland

    Heikki Hyöty

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  1. Kendra Vehik
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Consortia

the TEDDY Study Group

Contributions

K.V., K.F.L., M.R., J.T., A.G.Z., J.-X.S., A.L., B.A., W.A.H., D.A.S., J.P.K., H.H. and R.E.L. designed the study. M.R., W.A.H., J.T., A.G.Z., J.-X.S., B.A., A.L., H.H., K.V., K.F.L. and J.P.K. participated in patient recruitment and diagnosis, sample collection, and generation of the metadata. J.F.P., R.E.L., N.J.A., M.C.W., M.C.R., X.T. and R.A.G. generated and processed the raw sequencing data. K.F.L., K.V., H.H. and R.E.L. performed the data analysis, data interpretation and figure generation. K.V., K.F.L., H.H. and R.E.L. wrote the paper. All authors contributed to critical revisions and approved the final manuscript.

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Correspondence to Kendra Vehik.

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Competing interests

H.H. is a shareholder and chairman of the board of Vactech, and a member of the Scientific Advisory Board of Provention Bio, which develops vaccines against picornaviruses and CVB. The authors have no other relevant affiliations, nor financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

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Peer review information Jennifer Sargent was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Percentage of stool samples at age of first appearance of Enterovirus B.

Panel a shows sample positivity and Panel b sample consecutive positivity. Panels c and d show months prior to autoantibody seroconversion of Enterovirus B for sample positivity (c) and sample consecutive positivity (d) by autoantibody case status (n=383 matched pair children). Blue line represents control samples and red line represents case samples. The timing of the first appearance of an Enterovirus B infection from enrollment (3 months of age) or months prior to islet autoimmunity showed no obvious trend by age of child.

Source data

Extended Data Fig. 2 Common human viruses related to type 1 diabetes (T1D).

The three forest plots (a-c) show how common human viruses relate to the odds of children being diagnosed with T1D. The results were shown as odds ratios (OR, circle) and 95% confidence intervals (CI, bars) and were calculated using conditional logistic regression models with adjustment for HLA-DR-DQ genotype. OR>1 indicated a positive correlation between virus pattern and diagnosis with T1D, OR<1 indicated an inverse correlation. Plot (a) examined if an increase in the number of samples positive for virus was correlated with T1D (n=112 matched pair children). Plot (b) examined if children positive for the virus between 3 and 6 months of age were related to T1D (n=103 matched pair children). Plot (c) examined if children positive for the common virus in at least two consecutive samples (yes versus no) were related to T1D (n=112 matched pair children). Black circles and CI bars represent non-significant associations. Red circles and CI bars represent significant association with T1D. The number of positive stool samples for Enterovirus B was lower among T1D cases compared to matched controls. Human mastadenovirus C, similar to islet autoimmunity cases, was less likely to be detected in early stool samples (3–6 months of age) compared to the matched control for T1D cases. All p-values were two-sided.

Source data

Extended Data Fig. 3 Heatmaps of contig alignments of successive stools (n=6 children).

Heatmaps showing percent homology of alignments of enterovirus contigs isolated from successive stools from the same child. Stool collection date (successive days in the study) are shown, the serotype for the enterovirus aligned, all are aligned to an enterovirus genome map with scale of nucleotides at the bottom. Heatmap color is assigned on ~7 nt/pixel, heatmap color scale of percent homology is shown at the top.

Extended Data Fig. 4 Children consecutive positive for Enterovirus B with prolonged shedding of same serotype.

Categorical months of shedding by number of children for islet autoimmunity cases (n=45) and controls (n=25). Red bars denote cases and blue bars denote controls. Length of prolonged shedding period (duration) was not associated with case status in the children with consecutive positive Enterovirus B. Conditional logistic regression was used to evaluate significance; test was two-sided.

Source data

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Vehik, K., Lynch, K.F., Wong, M.C. et al. Prospective virome analyses in young children at increased genetic risk for type 1 diabetes. Nat Med 25, 1865–1872 (2019). https://doi.org/10.1038/s41591-019-0667-0

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