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Abstract
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
Here, by characterizing gut metagenomes of at-risk children in the TEDDY project, the authors associate onset of autoimmunity leading to Type-1 diabetes with certain sets of microorganisms in the gut microbiota, and identify metabolic capabilities encoded in the genomes of these microorganisms that provide functional insights to the association.
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Details
; Zhang, Zuyuan 3 ; Xiong, Yi 4 ; Mueller, Ryan S. 5 ; Friedman, Jacob E. 6 ; Pan, Chongle 7
1 University of Oklahoma Health Sciences Center, Harold Hamm Diabetes Center, Oklahoma City, USA (GRID:grid.266902.9) (ISNI:0000 0001 2179 3618); University of Oklahoma, Department of Microbiology and Plant Biology, Norman, USA (GRID:grid.266900.b) (ISNI:0000 0004 0447 0018)
2 University of Oklahoma Health Sciences Center, Harold Hamm Diabetes Center, Oklahoma City, USA (GRID:grid.266902.9) (ISNI:0000 0001 2179 3618); University of Oklahoma Health Sciences Center, Department of Biochemistry and Molecular Biology, Oklahoma City, USA (GRID:grid.266902.9) (ISNI:0000 0001 2179 3618)
3 University of Oklahoma, School of Computer Science, Norman, USA (GRID:grid.266900.b) (ISNI:0000 0004 0447 0018)
4 University of Oklahoma, Department of Microbiology and Plant Biology, Norman, USA (GRID:grid.266900.b) (ISNI:0000 0004 0447 0018)
5 Oregon State University, Department of Microbiology, Corvallis, USA (GRID:grid.4391.f) (ISNI:0000 0001 2112 1969)
6 University of Oklahoma Health Sciences Center, Harold Hamm Diabetes Center, Oklahoma City, USA (GRID:grid.266902.9) (ISNI:0000 0001 2179 3618); University of Oklahoma Health Sciences Center, Department of Biochemistry and Molecular Biology, Oklahoma City, USA (GRID:grid.266902.9) (ISNI:0000 0001 2179 3618); University of Oklahoma Health Sciences Center, Department of Physiology, Oklahoma City, USA (GRID:grid.266902.9) (ISNI:0000 0001 2179 3618)
7 University of Oklahoma Health Sciences Center, Harold Hamm Diabetes Center, Oklahoma City, USA (GRID:grid.266902.9) (ISNI:0000 0001 2179 3618); University of Oklahoma, Department of Microbiology and Plant Biology, Norman, USA (GRID:grid.266900.b) (ISNI:0000 0004 0447 0018); University of Oklahoma, School of Computer Science, Norman, USA (GRID:grid.266900.b) (ISNI:0000 0004 0447 0018)




