Toward defining the autoimmune microbiome for type 1 diabetes - PubMed (original) (raw)

doi: 10.1038/ismej.2010.92. Epub 2010 Jul 8.

Kelsey A Gano, David B Crabb, Nabanita Mukherjee, Luis L Novelo, George Casella, Jennifer C Drew, Jorma Ilonen, Mikael Knip, Heikki Hyöty, Riitta Veijola, Tuula Simell, Olli Simell, Josef Neu, Clive H Wasserfall, Desmond Schatz, Mark A Atkinson, Eric W Triplett

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Toward defining the autoimmune microbiome for type 1 diabetes

Adriana Giongo et al. ISME J. 2011 Jan.

Abstract

Several studies have shown that gut bacteria have a role in diabetes in murine models. Specific bacteria have been correlated with the onset of diabetes in a rat model. However, it is unknown whether human intestinal microbes have a role in the development of autoimmunity that often leads to type 1 diabetes (T1D), an autoimmune disorder in which insulin-secreting pancreatic islet cells are destroyed. High-throughput, culture-independent approaches identified bacteria that correlate with the development of T1D-associated autoimmunity in young children who are at high genetic risk for this disorder. The level of bacterial diversity diminishes overtime in these autoimmune subjects relative to that of age-matched, genotype-matched, nonautoimmune individuals. A single species, Bacteroides ovatus, comprised nearly 24% of the total increase in the phylum Bacteroidetes in cases compared with controls. Conversely, another species in controls, represented by the human firmicute strain CO19, represented nearly 20% of the increase in Firmicutes compared with cases overtime. Three lines of evidence are presented that support the notion that, as healthy infants approach the toddler stage, their microbiomes become healthier and more stable, whereas, children who are destined for autoimmunity develop a microbiome that is less diverse and stable. Hence, the autoimmune microbiome for T1D may be distinctly different from that found in healthy children. These data also suggest bacterial markers for the early diagnosis of T1D. In addition, bacteria that negatively correlated with the autoimmune state may prove to be useful in the prevention of autoimmunity development in high-risk children.

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Figures

Figure 1

Figure 1

Significant differences in taxa between cases (autoimmune) and controls (healthy). Samples were collected approximately 4 months, 1 year and 2 years after birth, represented, respectively, as time points 1, 2 and 3: (a) increasing numbers of Bacteroidetes in cases overtime compared with controls; (b) increasing numbers of Firmicutes in controls overtime compared with cases; and (c) higher proportion of unclassified sequences in controls compared with cases. Significant differences between cases and controls are designated by a star (_P_⩽0.002).

Figure 2

Figure 2

Bacterial community differences between cases and controls during autoimmunity development in cases; (a) significant increase in Bacteroidetes with concomitant decrease in Firmicutes in cases compared with controls (_P_-value ⩽0.01 at all time points; (b) significantly higher (P<0.05) Shannon diversity index in controls compared with cases in time point 3. Significant differences between cases and controls are designated by a star. The _P_-values for time points 1, 2 and 3 are (a) 0.0000, 0.0000 and 0.0000, and (b) 0.80, 0.33 and 0.03, respectively.

Figure 3

Figure 3

Histograms showing the permutation test based on the UniFrac significance obtained from the three time points (a, b and c). Dashed blue lines represent the 0.10, 0.05 and 0.01 quantiles, and the red line indicates the value of the observed difference. No differences in community diversity were observed at time point 1 at the 10% confidence interval. However, the average distance between any pair of cases was significantly higher than that between any pair of controls at the second and third collection points at the 5% and 10% level of confidence, respectively. A summary of data over all time points is shown in (d). (The color version of this figure is available in online version only).

Figure 4

Figure 4

Principal coordinate analysis for the case and control communities at time points 1 (a), 2 (b) and 3 (c).

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