The early life microbiome plays important roles in host immunological and metabolic development. Because the incidence of type 1 diabetes (T1D) has been increasing substantially in recent decades, we hypothesized that early-life antibiotic use alters gut microbiota, which predisposes to disease. Using non-obese diabetic mice that are genetically susceptible to T1D, we examined the effects of exposure to either continuous low-dose antibiotics or pulsed therapeutic antibiotics (PAT) early in life, mimicking childhood exposures. We found that in mice receiving PAT, T1D incidence was significantly higher, and microbial community composition and structure differed compared with controls. In pre-diabetic male PAT mice, the intestinal lamina propria had lower Th17 and Treg proportions and intestinal SAA expression than in controls, suggesting key roles in transducing the altered microbiota signals. PAT affected microbial lipid metabolism and host cholesterol biosynthetic gene expression. These findings show that early-life antibiotic treatments alter the gut microbiota and its metabolic capacities, intestinal gene expression and T-cell populations, accelerating T1D onset in non-obese diabetic mice.
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Diamond Project Group. Incidence and trends of childhood type 1 diabetes worldwide 1990–1999. Diabetic Med. 23, 857–866 (2006).
Patterson, C. C. et al. Incidence trends for childhood type 1 diabetes in Europe during 1989–2003 and predicted new cases 2005–20: a multicentre prospective registration study. Lancet 373, 2027–2033 (2009).
Olszak, T. et al. Microbial exposure during early life has persistent effects on natural killer T cell function. Science 336, 489–493 (2012).
Azad, M. B. & Kozyrskyj, A. L. Perinatal programming of asthma: the role of gut microbiota. Clin. Dev. Immunol. 2012, 932072 (2012).
Kozyrskyj, A. L., Ernst, P. & Becker, A. B. Increased risk of childhood asthma from antibiotic use in early life. Chest 131, 1753–1759 (2007).
Thavagnanam, S., Fleming, J., Bromley, A., Shields, M. D. & Cardwell, C. R. A meta-analysis of the association between caesarean section and childhood asthma. Clin. Exp. Allergy 38, 629–633 (2008).
Clemente, J. C. et al. The microbiome of the uncontacted Amerindians. Sci. Adv. 1, e1500183 (2015).
Kostic, A. D. et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe 17, 260–273 (2015).
Boursi, B., Mamtani, R., Haynes, K. & Yang, Y. X. The effect of past antibiotic exposure on diabetes risk. Eur. J. Endocrinol. 172, 639–648 (2015).
Pozzilli, P., Signore, A., Williams, A. J. & Beales, P. E. NOD mouse colonies around the world—recent facts and figures. Immunol. Today 14, 193–196 (1993).
Wen, L. et al. Innate immunity and intestinal microbiota in the development of type 1 diabetes. Nature 455, 1109–1113 (2008).
Leiter, E. H. The NOD mouse: a model for insulin-dependent diabetes mellitus. Curr. Protoc. Immunol. Ch. 15, Unit 15.9 (2001).
Cho, I. et al. Antibiotics in early life alter the murine colonic microbiome and adiposity. Nature 488, 621–626 (2012).
Cox, L. M. et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell 158, 705–721 (2014).
Nobel, Y. R. et al. Metabolic and metagenomic outcomes from early-life pulsed antibiotic treatment. Nat. Commun. 6, 7486 (2015).
Candon, S. et al. Antibiotics in early life alter the gut microbiome and increase disease incidence in a spontaneous mouse model of autoimmune insulin-dependent diabetes. PLoS ONE 10, e0125448 (2015).
Hu, Y. et al. Maternal antibiotic treatment protects offspring from diabetes development in nonobese diabetic mice by generation of tolerogenic APCs. J. Immunol. 195, 4176–4184 (2015).
Brown, K. et al. Prolonged antibiotic treatment induces a diabetogenic intestinal microbiome that accelerates diabetes in NOD mice. ISME J. 10, 321–332 (2016).
Atarashi, K. et al. Induction of colonic regulatory T cells by indigenous Clostridium species. Science 331, 337–341 (2011).
Ivanov, I. I. et al. Induction of intestinal Th17 cells by segmented filamentous bacteria. Cell 139, 485–498 (2009).
Markle, J. G. et al. Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity. Science 339, 1084–1088 (2013).
Lund, R. J. et al. Genome-wide identification of novel genes involved in early Th1 and Th2 cell differentiation. J. Immunol. 178, 3648–3660 (2007).
Greiner, T. U., Hyotylainen, T., Knip, M., Backhed, F. & Oresic, M. The gut microbiota modulates glycaemic control and serum metabolite profiles in non-obese diabetic mice. PLoS ONE 9, e110359 (2014).
Chai, G. et al. Trends of outpatient prescription drug utilization in US children, 2002–2010. Pediatrics 130, 23–31 (2012).
Hansen, C. H. et al. Early life treatment with vancomycin propagates Akkermansia muciniphila and reduces diabetes incidence in the NOD mouse. Diabetologia 55, 2285–2294 (2012).
Tormo-Badia, N. et al. Antibiotic treatment of pregnant non-obese diabetic mice leads to altered gut microbiota and intestinal immunological changes in the offspring. Scand. J. Immunol. 80, 250–260 (2014).
Larsson, E. et al. Analysis of gut microbial regulation of host gene expression along the length of the gut and regulation of gut microbial ecology through MyD88. Gut 61, 1124–1131 (2012).
Buckner, J. H. Mechanisms of impaired regulation by CD4+CD25+FOXP3+ regulatory T cells in human autoimmune diseases. Nat. Rev. Immunol. 10, 849–859 (2010).
Badami, E. et al. Defective differentiation of regulatory FoxP3+ T cells by small-intestinal dendritic cells in patients with type 1 diabetes. Diabetes 60, 2120–2124 (2011).
Bedoya, S. K., Lam, B., Lau, K. & Larkin, J. III Th17 cells in immunity and autoimmunity. Clin. Dev. Immunol. 2013, 986789 (2013).
Emamaullee, J. A. et al. Inhibition of Th17 cells regulates autoimmune diabetes in NOD mice. Diabetes 58, 1302–1311 (2009).
Kriegel, M. A. et al. Naturally transmitted segmented filamentous bacteria segregate with diabetes protection in nonobese diabetic mice. Proc. Natl Acad. Sci. USA 108, 11548–11553 (2011).
Lau, K. et al. Inhibition of type 1 diabetes correlated to a Lactobacillus johnsonii N6.2-mediated Th17 bias. J. Immunol. 186, 3538–3546 (2011).
Blaschitz, C. & Raffatellu, M. Th17 cytokines and the gut mucosal barrier. J. Clin. Immunol. 30, 196–203 (2010).
Bosi, E. et al. Increased intestinal permeability precedes clinical onset of type 1 diabetes. Diabetologia 49, 2824–2827 (2006).
Giongo, A. et al. Toward defining the autoimmune microbiome for type 1 diabetes. ISME J. 5, 82–91 (2011).
Turroni, F., van Sinderen, D. & Ventura, M. Genomics and ecological overview of the genus Bifidobacterium. Int. J. Food Microbiol. 149, 37–44 (2011).
Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).
Cardwell, C. R. et al. Breast-feeding and childhood-onset type 1 diabetes: a pooled analysis of individual participant data from 43 observational studies. Diabetes Care 35, 2215–2225 (2012).
Cardwell, C. R. et al. Caesarean section is associated with an increased risk of childhood-onset type 1 diabetes mellitus: a meta-analysis of observational studies. Diabetologia 51, 726–735 (2008).
De Goffau, M. C. et al. Fecal microbiota composition differs between children with β-cell autoimmunity and those without. Diabetes 62, 1238–1244 (2013).
Salzman, N. H. et al. Analysis of 16S libraries of mouse gastrointestinal microflora reveals a large new group of mouse intestinal bacteria. Microbiology 148, 3651–3660 (2002).
Grapov, D. et al. Diabetes associated metabolomic perturbations in NOD mice. Metabolomics 11, 425–437 (2015).
Oresic, M. et al. Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes. J. Exp. Med. 205, 2975–2984 (2008).
Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).
Martin, F. P. et al. A top-down systems biology view of microbiome-mammalian metabolic interactions in a mouse model. Mol. Syst. Biol. 3, 112 (2007).
Youssef, S. et al. The HMG-CoA reductase inhibitor, atorvastatin, promotes a Th2 bias and reverses paralysis in central nervous system autoimmune disease. Nature 420, 78–84 (2002).
Furusawa, Y. et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature 504, 446–450 (2013).
Brown, C. T. et al. Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes. PLoS ONE 6, e25792 (2011).
Lowell, C. A., Stearman, R. S. & Morrow, J. F. Transcriptional regulation of serum amyloid A gene expression. J. Biol. Chem. 261, 8453–8461 (1986).
Harkness, J. E. & Wagner, J. E. The Biology and Medicine of Rabbits and Rodents (Lea & Febiger, 1989).
Jukes, T. H. The present status and background of antibiotics in the feeding of domestic animals. Ann. NY Acad. Sci. 182, 362–379 (1971).
Lewicki, J. Tylosin: a review of pharmacokinetics, residues in food animals and analytical methods (United Nations Food and Agriculture Organization, 2006); ftp://ftp.fao.org/ag/agn/food/tylosin_2006.pdf
Ize-Ludlow, D. et al. Progressive erosion of β-cell function precedes the onset of hyperglycemia in the NOD mouse model of type 1 diabetes. Diabetes 60, 2086–2091 (2011).
Forestier, C. et al. Improved outcomes in NOD mice treated with a novel Th2 cytokine-biasing NKT cell activator. J. Immunol. 178, 1415–1425 (2007).
Wilson, J. R. & Koehler, K. J. Testing of equality of vectors of proportions for several cluster samples. Proc. Joint Statist. Assoc. Meet. Surv. Res. Meth. 39, 201–206 (1984).
La Rosa, P. S. et al. Hypothesis testing and power calculations for taxonomic-based human microbiome data. PLoS ONE 7, e52078 (2012).
Irizarry, R. A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).
Smyth, G. K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statist. Appl. Genet. Mol. Biol. 3, 1–25 (2004).
Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).
Kramer, A., Green, J., Pollard, J. Jr & Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30, 523–530 (2014).
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287 (2012).
Beckonert, O. et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2, 2692–2703 (2007).
Le Gall, G. et al. Metabolomics of fecal extracts detects altered metabolic activity of gut microbiota in ulcerative colitis and irritable bowel syndrome. J. Proteome Res. 10, 4208–4218 (2011).
Banerjee, R., Pathmasiri, W., Snyder, R., McRitchie, S. & Sumner, S. Metabolomics of brain and reproductive organs: characterizing the impact of gestational exposure to butylbenzyl phthalate on dams and resultant offspring. Metabolomics 8, 1012–1025 (2012).
Church, R. J. et al. A systems biology approach utilizing a mouse diversity panel identifies genetic differences influencing isoniazid-induced microvesicular steatosis. Toxicol. Sci. 140, 481–492 (2014).
Pathmasiri, W. et al. Integrating metabolomic signatures and psychosocial parameters in responsivity to an immersion treatment model for adolescent obesity. Metabolomics 8, 1037–1051 (2012).
Sumner, S. et al. Metabolomics in the assessment of chemical-induced reproductive and developmental outcomes using non-invasive biological fluids application to the study of butylbenzyl phthalate. J. Appl. Toxicol. 29, 703–714 (2009).
Sumner, S. C., Fennell, T. R., Snyder, R. W., Taylor, G. F. & Lewin, A. H. Distribution of carbon-14 labeled C60 ([14C]C60) in the pregnant and in the lactating dam and the effect of C60 exposure on the biochemical profile of urine. J. Appl. Toxicol. 30, 354–360 (2010).
Sumner, S. J., Burgess, J. P., Snyder, R. W., Popp, J. A. & Fennell, T. R. Metabolomics of urine for the assessment of microvesicular lipid accumulation in the liver following isoniazid exposure. Metabolomics 6, 238–249 (2010).
Ivanov, I. I. et al. The orphan nuclear receptor RORγt directs the differentiation program of proinflammatory IL-17+ T helper cells. Cell 126, 1121–1133 (2006).
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).
Aronesty, E. Comparison of sequence utility programs. Open Bioinformatics 7, 1–8 (2013).
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).
McArdle, B. H. & Anderson, M. J. Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82, 290–297 (2001).
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
Matsuki, T. et al. Quantitative PCR with 16S rRNA-gene-targeted species specific primers for analysis of human intestinal bifidobacteria. Appl. Environ. Microbiol. 70, 167–173 (2004).
Barman, M. et al. Enteric salmonellosis disrupts the microbial ecology of the murine gastrointestinal tract. Infect. Immun. 76, 907–915 (2008).
Breiman, L. Random forests. Machine Learning 45, 5–32 (2001).
Breiman, L. Manual on setting up, using, and understanding random forests v3.1 (2002); https://www.stat.berkeley.edu/~breiman/Using_random_forests_V3.1.pdf
Knights, D., Costello, E. K. & Knight, R. Supervised classification of human microbiota. FEMS Microbiol. Rev. 35, 343–359 (2011).
Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).
Parks, D. H. & Beiko, R. G. Identifying biologically relevant differences between metagenomic communities. Bioinformatics 26, 715–721 (2010).
Research support funding was provided by the Juvenile Diabetes Research Foundation, the Diane Belfer Program for Human Microbial Ecology, the Knapp Family, the Ziff Family and C&D Funds (to M.J.B.), the Howard Hughes Medical Institute and the Defendi Fellowship (to A.E.L.). Sequencing was performed at the NYUMC Genome Technology Center, partially supported by a Cancer Center Support Grant (P30CA016087) at the Laura and Isaac Perlmutter Cancer Center. The authors thank T. Battaglia and P. Meyn for informatic and technical assistance.
The authors declare no competing financial interests.
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Livanos, A., Greiner, T., Vangay, P. et al. Antibiotic-mediated gut microbiome perturbation accelerates development of type 1 diabetes in mice. Nat Microbiol 1, 16140 (2016). https://doi.org/10.1038/nmicrobiol.2016.140
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