Massive parallel sequencing provides new perspectives on bacterial brain abscesses.

Rapid development within the field of massive parallel sequencing (MPS) is about to bring this technology within reach for diagnostic microbiology laboratories. We wanted to explore its potential for improving diagnosis and understanding of polymicrobial infections, using bacterial brain abscesses as an example. We conducted a prospective nationwide study on bacterial brain abscesses. Fifty-two surgical samples were included over a 2-year period. The samples were categorized as either spontaneous intracerebral, spontaneous subdural, or postoperative. Bacterial 16S rRNA genes were amplified directly from the specimens and sequenced using Ion Torrent technology, with an average of 500,000 reads per sample. The results were compared to those from culture- and Sanger sequencing-based diagnostics. Compared to culture, MPS allowed for triple the number of bacterial identifications. Aggregatibacter aphrophilus, Fusobacterium nucleatum, and Streptococcus intermedius or combinations of them were found in all spontaneous polymicrobial abscesses. F. nucleatum was systematically detected in samples with anaerobic flora. The increased detection rate for Actinomyces spp. and facultative Gram-negative rods further revealed several species associations. We suggest that A. aphrophilus, F. nucleatum, and S. intermedius are key pathogens for the establishment of spontaneous polymicrobial brain abscesses. In addition, F. nucleatum seems to be important for the development of anaerobic flora. MPS can accurately describe polymicrobial specimens when a sufficient number of reads is used to compensate for unequal species concentrations and principles are defined to discard contaminant bacterial DNA in the subsequent data analysis. This will contribute to our understanding of how different types of polymicrobial infections develop.

Simultaneous sequence analysis of the 16S rRNA and rpoB genes by use of RipSeq software to identify Mycobacterium species.

The 16S rRNA gene is commonly used to identify Mycobacterium spp., but alternative DNA targets can provide better resolution to the species level. We evaluated a novel system that enables simultaneous amplification, sequencing, and analysis of two different DNA targets in a single tube to identify clinical isolates of Mycobacterium spp. For 139 clinical isolates, we found that the 16S rRNA gene alone identified 67 (48%) isolates as single species, 68 (49%) isolates to the complex or group level, and 4 (3%) isolates to the genus level only. The rpoB gene alone identified 117 (84%) isolates as single species, 10 (7%) isolates to the complex or group level, and 12 (8%) isolates to the genus level only. Combining the separate analyses for sequencing of two gene targets, 119 (86%) isolates were identified as single species and 10 (7%) isolates were identified to the complex or group level. Seven (5%) isolates were identified as novel species within established groups, and 3 (2%) were identified to the genus level only. Dual-locus identification identified 110 (79%) isolates as single species and 22 (16%) isolates to the complex or group level. Six (4%) were identified as novel species within established groups, and 1 (1%) was identified to the genus level only. Identifications were more accurate when both the 16S rRNA and rpoB genes were screened, and reliance on a single gene target was suboptimal. RipSeq dual-locus software provides an accurate alternative method for laboratories using two different gene targets for microorganism identification.

Validation and Retrospective Clinical Evaluation of a Quantitative 16S rRNA Gene Metagenomic Sequencing Assay for Bacterial Pathogen Detection in Body Fluids.

Next-generation sequencing-based 16S rRNA gene metagenomic sequencing (16S MG) technology has tremendous potential for improving diagnosis of bacterial infections given its quantitative capability and culture-independent approach. We validated and used a quantitative 16S MG assay to identify and quantify bacterial species in clinical samples from a wide spectrum of infections, including meningitis, septic arthritis, brain abscess, intra-abdominal abscess, soft tissue abscess, and pneumonia. Twenty clinical samples were tested, and 16S MG identified a total of 34 species, compared with 22 species and three descriptive findings identified by culture. 16S MG results matched culture results in 75% (15/20) of the samples but detected at least one more species in five samples, including one culture-negative cerebrospinal fluid sample that was found to contain Streptococcus intermedius. Shotgun metagenomic sequencing verified the presence of all additional species. The 16S MG assay is highly sensitive, with a limit of detection of 10 to 100 colony-forming units/mL. Other performance characteristics, including linearity, precision, and specificity, all met the requirements for a clinical test. This assay showed the advantages of accurate identification and quantification of bacteria in culture-negative and polymicrobial infections for which conventional microbiology methods are limited. It also showed promises to serve unmet clinical needs for solving difficult infectious diseases cases.

Analysis of mixed sequencing chromatograms and its application in direct 16S rRNA gene sequencing of polymicrobial samples.

Investigation of clinical samples by direct 16S rRNA gene sequencing provides the possibility to detect nonviable bacteria and bacteria with special growth requirements. This approach has been particularly valuable for the diagnosis of patients who have received antibiotics prior to sample collection. In specimens containing more than one bacterium, direct sequencing gives mixed chromatograms that complicate further interpretation. We designed an algorithm able to analyze these ambiguous chromatograms and implemented it as a Web-based service. The algorithm contains both a new base-calling procedure and a new database search procedure. 16S rRNA gene sequencing was performed on polybacterial suspensions prepared in the laboratory. The computer program identified all bacteria correctly to the species level in 23 out of 23 samples containing two different bacteria. For samples containing three different bacteria, correct identification to the species level was achieved for three out of five and to the genus level for five out of five.