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LaGasse, J. M. et al. Successful prospective prediction of type 1 diabetes in schoolchildren through multiple defined autoantibodies: an 8-year follow-up of the Washington State Diabetes Prediction Study. Diabetes Care 25, 505–511 (2002).
Bonifacio, E. et al. Effects of high-dose oral insulin on immune responses in children at high risk for type 1 diabetes. J. Am. Med. Assoc. 313, 1541–1549 (2015). Cortes, A. & Brown, M. A. Promise and pitfalls of the immunochip. Arthritis Res. Ther. 13, 101 (2011). The HMO gene cluster homologues between B. longum subsp. infantis and multiple taxa were analysed as follows. UniRef90 gene families corresponding to the protein sequences in the B. longum subsp. infantis HMO gene cluster 39 (protein sequences Blon_2331-Blon_2361 in NCBI protein sequence database) were identified by translated BLAST search against ChocoPhlAn pangenome collection 48 used by HUMAnN2. Identified hits were further filtered by requiring ≥50% alignment identity and ≥80% mutual coverage. Combining this information with HUMAnN2 species-stratified UniRef90 gene family quantification enabled calling these genes present given that they had sufficient read coverage, here defined as log 10(counts per million) > 0.1 in at least 50 samples collected during breastfeeding. Differential gene prevalence during breastfeeding was tested using the samples in which the carrier species had >1% relative abundance. Testing was conducted using the test of equal or given proportions (prop.test function in R) and by comparing the prevalence (proportion of the samples for which the species in question harboured the gene according to the metagenomic data) of the gene in samples collected during breastfeeding with the samples collected after weaning. P values were adjusted for multiple testing by Benjamini–Hochberg method (p.adjust function in R). All homologues together with their BLAST search metrics, prevalence in the metagenomic data and corresponding B. infantis HMO gene are reported in Supplementary Table 5. Bacterial growth assays National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USAVatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165, 842–853 (2016). Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).
This study includes extensive collection of clinical covariates that cover several aspects of common and rare life events in early childhood from infancy up to five years of age. In these analyses, we used information that is, according to the literature, of high relevance in terms of gut microbiome development. Information about mothers, pregnancy and birth was collected during the three-month clinic visit by questionnaire and included the mode of birth (vaginal birth versus caesarean section), gestational age, infant’s 5-min Apgar score, information about maternal diabetes (T1D, T2D or gestational diabetes) and maternal insulin and medication use (antibiotics, angiotensin-converting enzyme inhibitors, metformin, glyburide, antihypertensives) during pregnancy. Dietary information used in these analyses includes the start (and end) date for the following dietary compounds: breastfeeding, baby formula, cow’s milk, gluten, cereals, meat, vegetables and fruits. The start of solid food (anything other than breast milk or cow’s milk) was also analysed separately. The T1D-associated autoantibodies IAA, GADA and IA2A were analysed from serum samples collected at each clinic visit. In addition to IA, defined as persistent, confirmed autoantibody seropositivity, we analysed the data in terms of the persistency and cumulative frequency of autoantibodies (single or multiple autoantibodies). In TEDDY, all prescribed antibiotic courses are recorded. We further stratified these data by the type of antibiotic in five categories: amoxicillin, penicillin, cephalosporins, macrolide and other antibiotics. Information about probiotics covered the dates for starting and stopping probiotic supplementation, but not the specific types of probiotics used. In addition, sex, information about whether first degree relatives in family had T1D, and HLA haplotypes of the subjects were used in these analyses. Subjects screened from the general population were identified with high-risk alleles (89%) including: DRB1*04-DQA1*03-DQB1*03:02/DRB1*03-DQA1*05-DQB1*02:01 (DR3/4), DRB1*04-DQA1*03-DQB1*03:02/DRB1*04-DQA1*03-DQB1*03:02 (DR4/4), DRB1*04-DQA1*03-DQB1*03:02/DRB1*08-DQA1*04-DQB1*04:02 (DR4/8) and DRB1*03-DQA1*05-DQB1*02:01/DRB1*03-DQA1*05-DQB1*02:01 (DR3/3), plus six genotypes specific to first-degree relatives 28.Thurston, B., Dawson, K. A. & Strobel, H. J. Pentose utilization by the ruminal bacterium Ruminococcus albus. Appl. Environ. Microbiol. 60, 1087–1092 (1994). National Institutes of Health Consensus Development Panel National Institutes of Health Consensus Development Conference Statement: phenylketonuria: screening and management, October 16–18, 2000. Pediatrics 108, 972–982 (2001). Hippich, M. et al. Genetic contribution to the divergence in type 1 diabetes risk between children from the general population and children from affected families. Diabetes 68, 847–857 (2019).
Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland
Huang, K. et al. MetaRef: a pan-genomic database for comparative and community microbial genomics. Nucleic Acids Res. 42, D617–D624 (2014).
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