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Date

2022-06-21

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Creative Commons
Except where otherwise noted, this item's license is described as Attribution 4.0 International

The immune system of some genetically susceptible children can be triggered by certain environmental factors to produce islet autoantibodies (IA) against pancreatic β cells, which greatly increases their risk for Type-1 diabetes. An environmental factor under active investigation is the gut microbiome due to its important role in immune system education. Here, we study gut metagenomes that are de-novo-assembled in 887 at-risk children in the Environmental Determinants of Diabetes in the Young (TEDDY) project. Our results reveal a small set of core protein families, present in >50% of the subjects, which account for 64% of the sequencing reads. Time-series binning generates 21,536 high-quality metagenome-assembled genomes (MAGs) from 883 species, including 176 species that hitherto have no MAG representation in previous comprehensive human microbiome surveys. IA seroconversion is positively associated with 2373 MAGs and negatively with 1549 MAGs. Comparative genomics analysis identifies lipopolysaccharides biosynthesis in Bacteroides MAGs and sulfate reduction in Anaerostipes MAGs as functional signatures of MAGs with positive IA-association. The functional signatures in the MAGs with negative IA-association include carbohydrate degradation in lactic acid bacteria MAGs and nitrate reduction in Escherichia MAGs. Overall, our results show a distinct set of gut microorganisms associated with IA seroconversion and uncovered the functional genomics signatures of these IA-associated microorganisms.

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Keywords

Metagenomics, Type 1 diabetes

Citation

Zhang, L., Jonscher, K.R., Zhang, Z. et al. Islet autoantibody seroconversion in type-1 diabetes is associated with metagenome-assembled genomes in infant gut microbiomes. Nat Commun 13, 3551 (2022). https://doi.org/10.1038/s41467-022-31227-1

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Sponsorship

We appreciate the data and technical support provided by the TEDDY project, which is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). This work is supported by an R01 grant (R01AT011618) to C.P. and R.S.M. from National Center for Complementary & Integrative Health and National Institute of General Medical Sciences and a Team Science grant to K.R.J. and C.P. from Presbyterian Health Foundation of Oklahoma City and Harold Hamm Diabetes Center. The high-performance computing was provided by the OU Supercomputing Center for Education & Research (OSCER). Financial support was provided by the University of Oklahoma Libraries’ Open Access Fund.