Use of 16S rRNA Gene for Identification of a Broad Range of Clinically Relevant Bacterial Pathogens

<div><p>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.</p></div

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

<p>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.</p

Clinical identity, identification technique, and source of all the clinical isolates used in this study.

<p>Clinical identity, identification technique, and source of all the clinical isolates used in this study.</p

Concordance rates between 16S rRNA based and clinical identification for isolates clinically identified by culture-based or non-16S based molecular methods.

<p>Concordance rates between 16S rRNA based and clinical identification for isolates clinically identified by culture-based or non-16S based molecular methods.</p

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

<p>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).</p

Concordance rates between clinical and 16S rRNA based identification.

<p>16S based genus and species identities were through the use of the Naïve Bayes classifier and an alignment based approach (16SpathDB). For each clinical identity, in addition to the concordance rates, the number of concordant isolates among all isolates is listed in parentheses. For the latter method, the last two columns give the number of isolates with definite identification among the concordant isolates</p><p>* For species level comparisons, species for the clinical identifications <i>Enterobacter cloacae</i> complex and <i>Streptococcus viridans</i> group clinical were:</p><p><i>Enterobacter cloacae</i> complex <i>species</i>: <i>Enterobacter asburiae</i>, <i>Enterobacter cloacae</i>, <i>Enterobacter hormaechei</i>, <i>Enterobacter kobei</i>, <i>Enterobacter ludwigii</i>, <i>Enterobacter nimipressuralis</i>. <i>Streptococcus viridans</i> group species: (1) S. mitis group: S. mitis, S. sanguinis, S. parasanguinis, S. gordonii, S. oralis, S. cristatus, S. infantis, S. peroris, S. australis, S. oligofermentans, S. pneumoniae, S. pseudopneumoniae (2) S. mutans group: S. mutans, S. sobrinus (3) S. salivarius group: S. salivarius, S. vestibularis, S. thermophiles (4) S. bovis group: S. equinus, S. gallolyticus, S. infantarius, S. pasteurianus, and S. alactolyticus (5) S. anginosus group: S. anginosus, S. constellatus, S. intermedius.</p><p>Concordance rates between clinical and 16S rRNA based identification.</p

Bacterial identification by clinical microbiology laboratory techniques.

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

16S rRNA percent identity within and between genera.

<p>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).</p

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

<p>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).</p

Table_1_Synergistic Anti-Tumor Effect of Combining Selective CDK7 and BRD4 Inhibition in Neuroblastoma.xls

PurposeCyclin-dependent kinases (CDKs) that have critical roles in RNA polymerase II (Pol II)-mediated gene transcription are emerging as therapeutic targets in cancer. We have previously shown that THZ1, a covalent inhibitor of CDKs 7/12/13, leads to cytotoxicity in MYCN-amplified neuroblastoma through the downregulation of super-enhancer-associated transcriptional upregulation. Here we determined the effects of YKL-5-124, a novel covalent inhibitor with greater selectivity for CDK7 in neuroblastoma cells.Experimental DesignWe tested YKL-5-124 in MYCN-amplified and nonamplified neuroblastoma cells individually and in combination with other inhibitors in cell line and animal models. Cell viability, target validation, effects on cell cycle and transcription were analyzed.ResultsCDK7 inhibition with YKL-5-124 did not lead to significant cell death, but resulted in aberrant cell cycle progression especially in MYCN-amplified cells. Unlike THZ1, YKL-5-124 had minimal effects on Pol II C-terminal domain phosphorylation, but significantly inhibited that of the CDK1 and CDK2 cell cycle kinases. Combining YKL-5-124 with the BRD4 inhibitor JQ1 resulted in synergistic cytotoxicity. A distinct MYCN-gene expression signature associated with resistance to BRD4 inhibition was suppressed with the combination. The synergy between YKL-5-124 and JQ1 translated into significant tumor regression in cell line and patient-derived xenograft mouse models of neuroblastoma.ConclusionsThe combination of CDK7 and BRD4 inhibition provides a therapeutic option for neuroblastoma and suggests that the addition of YKL-5-124 could improve the therapeutic efficacy of JQ1 and delay resistance to BRD4 inhibition.</p