Use of 16S rRNA gene for identification of a broad range of clinically relevant bacterial pathogens

Abstract

According to World Health Organization statistics of 2011, infectious diseases remain in the top five causes of mortality worldwide. However, despite sophisticated research tools for microbial detection, rapid and accurate molecular diagnostics for identification of infection in humans have not been extensively adopted. Time-consuming culture-based methods remain to the forefront of clinical microbial detection. The 16S rRNA gene, a molecular marker for identification of bacterial species, is ubiquitous to members of this domain and, thanks to ever-expanding databases of sequence information, a useful tool for bacterial identification. In this study, we assembled an extensive repository of clinical isolates (n = 617), representing 30 medically important pathogenic species and originally identified using traditional culture-based or non-16S molecular methods. This strain repository was used to systematically evaluate the ability of 16S rRNA for species level identification. To enable the most accurate species level classification based on the paucity of sequence data accumulated in public databases, we built a Naïve Bayes classifier representing a diverse set of high-quality sequences from medically important bacterial organisms. We show that for species identification, a model-based approach is superior to an alignment based method. Overall, between 16S gene based and clinical identities, our study shows a genus-level concordance rate of 96% and a species-level concordance rate of 87.5%. We point to multiple cases of probable clinical misidentification with traditional culture based identification across a wide range of gram-negative rods and gram-positive cocci as well as common gram-negative cocci.

Fig 1. Bacterial identification by clinical microbiology laboratory techniques.

Typical temporal workflow of clinical microbiological laboratory to identify microbes from clinical samples based on phenotypic, biochemical, and culture-based techniques.

Fig 2. 16S rRNA percent identity within and between genera.

Distributions (shown as violin plots) of 16S rRNA percent identity (y-axis of each figure) of pairs of training set sequences belonging to the same (gray) and different genera. 95% identity, the traditional genus level cutoff, has been marked for reference. The genus Mycobacterium has been categorized as a gram-positive in the figure. For all of the genera, sequence variability between sequences from the same genera was significantly higher than between those from different genera for all comparisons (Wilcoxon test one-sided p-value<0.0001).

Fig 3. 16S rRNA percent identity within and between species (gram-positive bacteria).

Distributions (shown as violin plots) of 16S rRNA percent identity (y-axis of each figure) of pairs of training set sequences belonging to the same (gray) and different species for select gram-positive bacteria. 97% identity, the traditional species level cutoff, has been marked for reference. Species for which sequence variability between sequences from the same species was significantly higher than between those from different species are marked with a * (Wilcoxon test one-sided p-value<0.0001).

Fig 4. 16S rRNA percent identity within and between species (gram-negative bacteria).

Distributions (shown as violin plots) of 16S rRNA percent identity (y-axis of each figure) of pairs of training set sequences belonging to the same (gray) and different species for select gram-negative bacteria. 97% identity, the traditional species level cutoff, has been marked for reference. Species for which sequence variability between sequences from the same species was significantly higher than between those from different species are marked with a * (Wilcoxon test one-sided p-value<0.0001).

Fig 5. 16S rRNA based genus and species level isolate identities with the Naïve Bayes classifier.

Each isolate was assigned to one of 12 categories (A-H, a-d) based on the agreement between clinical and 16S rRNA based genus and species classifications and the confidence scores.