Estimates of the number of species of bacteria per gram of soil vary between 2000 and 8.3 million (Gans et al., 2005; Schloss and Handelsman, 2006). The highest estimate suggests that the number may be so large as to be impractical to test by amplification and sequencing of the highly conserved 16S rRNA gene from soil DNA (Gans et al., 2005). Here we present the use of high throughput DNA pyrosequencing and statistical inference to assess bacterial diversity in four soils across a large transect of the western hemisphere. The number of bacterial 16S rRNA sequences obtained from each site varied from 26 140 to 53 533. The most abundant bacterial groups in all four soils were the Bacteroidetes, Betaproteobacteria and Alphaproteobacteria. Using three estimators of diversity, the maximum number of unique sequences (operational taxonomic units roughly corresponding to the species level) never exceeded 52 000 in these soils at the lowest level of dissimilarity. Furthermore, the bacterial diversity of the forest soil was phylum rich compared to the agricultural soils, which are species rich but phylum poor. The forest site also showed far less diversity of the Archaea with only 0.009% of all sequences from that site being from this group as opposed to 4%–12% of the sequences from the three agricultural sites. This work is the most comprehensive examination to date of bacterial diversity in soil and suggests that agricultural management of soil may significantly influence the diversity of bacteria and archaea.

We report here the sequencing and analysis of the genome of the nitrogen-fixing endophyte, Klebsiella pneumoniae 342. Although K. pneumoniae 342 is a member of the enteric bacteria, it serves as a model for studies of endophytic, plant-bacterial associations due to its efficient colonization of plant tissues (including maize and wheat, two of the most important crops in the world), while maintaining a mutualistic relationship that encompasses supplying organic nitrogen to the host plant. Genomic analysis examined K. pneumoniae 342 for the presence of previously identified genes from other bacteria involved in colonization of, or growth in, plants. From this set, approximately one-third were identified in K. pneumoniae 342, suggesting additional factors most likely contribute to its endophytic lifestyle. Comparative genome analyses were used to provide new insights into this question. Results included the identification of metabolic pathways and other features devoted to processing plant-derived cellulosic and aromatic compounds, and a robust complement of transport genes (15.4%), one of the highest percentages in bacterial genomes sequenced. Although virulence and antibiotic resistance genes were predicted, experiments conducted using mouse models showed pathogenicity to be attenuated in this strain. Comparative genomic analyses with the presumed human pathogen K. pneumoniae MGH78578 revealed that MGH78578 apparently cannot fix nitrogen, and the distribution of genes essential to surface attachment, secretion, transport, and regulation and signaling varied between each genome, which may indicate critical divergences between the strains that influence their preferred host ranges and lifestyles (endophytic plant associations for K. pneumoniae 342 and presumably human pathogenesis for MGH78578). Little genome information is available concerning endophytic bacteria. The K. pneumoniae 342 genome will drive new research into this less-understood, but important category of bacterial-plant host relationships, which could ultimately enhance growth and nutrition of important agricultural crops and development of plant-derived products and biofuels.

The analysis of amplified and sequenced 16S rRNA genes has become the most important single approach for microbial diversity studies. The new sequencing technologies allow for sequencing thousands of reads in a single run and a cost-effective option is split into a single run across many samples. However for this type of investigation the key question that needs to be answered is how many samples can be sequenced without biasing the results due to lack of sequence representativeness? In this work we demonstrated that the level of sequencing effort used for analyzing soil microbial communities biases the results and determines the most effective type of analysis for small and large datasets. Many simulations were performed with four independent pyrosequencing-generated 16S rRNA gene libraries from different environments. The analysis performed here illustrates the lack of resolution of OTU-based approaches for datasets with low sequence coverage. This analysis should be performed with at least 90% of sequence coverage. Diversity index values increase with sample size making normalization of the number of sequences in all samples crucial. An important finding of this study was the advantage of phylogenetic approaches for examining microbial communities with low sequence coverage. However, if the environments being compared were closely related, a deeper sequencing would be necessary to detect the variation in the microbial composition.

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.