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.

Recent studies have suggested a bacterial role in the development of autoimmune disorders including type 1 diabetes (T1D). Over 30 billion nucleotide bases of Illumina shotgun metagenomic data were analyzed from stool samples collected from four pairs of matched T1D case-control subjects collected at the time of the development of T1D associated autoimmunity (i.e., autoantibodies). From these, approximately one million open reading frames were predicted and compared to the SEED protein database. Of the 3,849 functions identified in these samples, 144 and 797 were statistically more prevalent in cases and controls, respectively. Genes involved in carbohydrate metabolism, adhesions, motility, phages, prophages, sulfur metabolism, and stress responses were more abundant in cases while genes with roles in DNA and protein metabolism, aerobic respiration, and amino acid synthesis were more common in controls. These data suggest that increased adhesion and flagella synthesis in autoimmune subjects may be involved in triggering a T1D associated autoimmune response. Extensive differences in metabolic potential indicate that autoimmune subjects have a functionally aberrant microbiome. Mining 16S rRNA data from these datasets showed a higher proportion of butyrate-producing and mucin-degrading bacteria in controls compared to cases, while those bacteria that produce short chain fatty acids other than butyrate were higher in cases. Thus, a key rate-limiting step in butyrate synthesis is more abundant in controls. These data suggest that a consortium of lactate- and butyrate-producing bacteria in a healthy gut induce a sufficient amount of mucin synthesis to maintain gut integrity. In contrast, non-butyrate-producing lactate-utilizing bacteria prevent optimal mucin synthesis, as identified in autoimmune subjects.

Bacteria associated with the onset of type 1 diabetes in a rat model system were identified. In two experiments, stool samples were collected at three time points after birth from bio-breeding diabetes-prone (BB-DP) and bio-breeding diabetes-resistant (BB-DR) rats. DNA was isolated from these samples and the 16S rRNA gene was amplified using universal primer sets. In the first experiment, bands specific to BB-DP and BB-DR genotypes were identified by automated ribosomal intergenic spacer analysis at the time of diabetes onset in BB-DP. Lactobacillus and Bacteroides strains were identified in the BB-DR- and BB-DP-specific bands, respectively. Sanger sequencing showed that the BB-DP and BB-DR bacterial communities differed significantly but too few reads were available to identify significant differences at the genus or species levels. A second experiment confirmed these results using higher throughput pyrosequencing and quantitative PCR of 16S rRNA with more rats per genotype. An average of 4541 and 3381 16S rRNA bacterial reads were obtained from each of the 10 BB-DR and 10 BB-DP samples collected at time of diabetes onset. Nine genera were more abundant in BB-DP whereas another nine genera were more abundant in BB-DR. Thirteen and eleven species were more abundant in BB-DP and BB-DR, respectively. An average of 23% and 10% of all reads could be classified at the genus and species levels, respectively. Quantitative PCR verified the higher abundance of Lactobacillus and Bifidobacterium in the BB-DR samples. Whether these changes are caused by diabetes or are involved in the development of the disease is unknown.

High-throughput DNA sequencing can identify organisms and describe population structures in many environmental and clinical samples. Current technologies generate millions of reads in a single run, requiring extensive computational strategies to organize, analyze and interpret those sequences. A series of bioinformatics tools for high-throughput sequencing analysis, including pre-processing, clustering, database matching and classification, have been compiled into a pipeline called PANGEA. The PANGEA pipeline was written in Perl and can be run on Mac OSX, Windows or Linux. With PANGEA, sequences obtained directly from the sequencer can be processed quickly to provide the files needed for sequence identification by BLAST and for comparison of microbial communities. Two different sets of bacterial 16S rRNA sequences were used to show the efficiency of this workflow. The first set of 16S rRNA sequences is derived from various soils from Hawaii Volcanoes National Park. The second set is derived from stool samples collected from diabetes-resistant and diabetes-prone rats. The workflow described here allows the investigator to quickly assess libraries of sequences on personal computers with customized databases. PANGEA is provided for users as individual scripts for each step in the process or as a single script where all processes, except the χ2 step, are joined into one program called the ‘backbone’.

Ancient mariners knew that dust whipped up from deserts by strong winds travelled long distances, including over oceans. Satellite remote sensing revealed major dust sources across the Sahara. Indeed, the Bodélé Depression in the Republic of Chad has been called the dustiest place on earth. We analysed desert sand from various locations in Chad and dust that had blown to the Cape Verde Islands. High throughput sequencing techniques combined with classical microbiological methods showed that the samples contained a large variety of microbes well adapted to the harsh desert conditions. The most abundant bacterial groupings in four different phyla included: (a) Firmicutes—Bacillaceae, (b) Actinobacteria—Geodermatophilaceae, Nocardiodaceae and Solirubrobacteraceae, (c) Proteobacteria—Oxalobacteraceae, Rhizobiales and Sphingomonadaceae, and (d) Bacteroidetes—Cytophagaceae. Ascomycota was the overwhelmingly dominant fungal group followed by Basidiomycota and traces of Chytridiomycota, Microsporidia and Glomeromycota. Two freshwater algae (Trebouxiophyceae) were isolated. Most predominant taxa are widely distributed land inhabitants that are common in soil and on the surfaces of plants. Examples include Bradyrhizobium spp. that nodulate and fix nitrogen in Acacia species, the predominant trees of the Sahara as well as Herbaspirillum (Oxalobacteraceae), a group of chemoorganotrophic free-living soil inhabitants that fix nitrogen in association with Gramineae roots. Few pathogenic strains were found, suggesting that African dust is not a large threat to public health.