In silico analysis of 16S rRNA gene sequencing based methods for identification of medically important aerobic Gram-negative bacteria

Genomic analysis of Elizabethkingia species from aquatic environments: Evidence for potential clinical transmission

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Identification of closely related (< 50 SNV) clinical and environmental aquatic Elizabethkingia anophelis isolates.

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Identification of a provisional novel species Elizabethkingia umaracha.

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Novel bla_GOB and bla_B carbapenemases and extended spectrum β-lactamase bla_CME alleles identified in Elizabethkingia spp.

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Analysis of the global phylogeny and pangenome of Elizabethkingia spp.

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Identification of novel ICE elements carrying uncharacterised genetic cargo in 67 / 94 (71.3%) of the aquatic environments Elizabethkingia spp.

Elizabethkingia species are ubiquitous in aquatic environments, colonize water systems in healthcare settings and are emerging opportunistic pathogens with reports surfacing in 25 countries across six continents. Elizabethkingia infections are challenging to treat, and case fatality rates are high. Chromosomal bla_B, bla_GOB and bla_CME genes encoding carbapenemases and cephalosporinases are unique to Elizabethkingia spp. and reports of concomitant resistance to aminoglycosides, fluoroquinolones and sulfamethoxazole-trimethoprim are known. Here, we characterized whole-genome sequences of 94 Elizabethkingia isolates carrying multiple wide-spectrum metallo-β-lactamase (bla_Band bla_GOB) and extended-spectrum serine‑β-lactamase (bla_CME) genes from Australian aquatic environments and performed comparative phylogenomic analyses against national clinical and international strains. qPCR was performed to quantify the levels of Elizabethkingia species in the source environments. Antibiotic MIC testing revealed significant resistance to carbapenems and cephalosporins but susceptibility to fluoroquinolones, tetracyclines and trimethoprim-sulfamethoxazole. Phylogenetics show that three environmental E. anophelis isolates are closely related to E. anophelis from Australian clinical isolates (∼36 SNPs), and a new species, E. umeracha sp. novel, was discovered. Genomic signatures provide insight into potentially shared origins and a capacity to transfer mobile genetic elements with both national and international isolates.

Performance and Application of 16S rRNA Gene Cycle Sequencing for Routine Identification of Bacteria in the Clinical Microbiology Laboratory

This review provides a state-of-the-art description of the performance of Sanger cycle sequencing of the 16S rRNA gene for routine identification of bacteria in the clinical microbiology laboratory. A detailed description of the technology and current methodology is outlined with a major focus on proper data analyses and interpretation of sequences. The remainder of the article is focused on a comprehensive evaluation of the application of this method for identification of bacterial pathogens based on analyses of 16S multialignment sequences. In particular, the existing limitations of similarity within 16S for genus- and species-level differentiation of clinically relevant pathogens and the lack of sequence data currently available in public databases is highlighted. A multiyear experience is described of a large regional clinical microbiology service with direct 16S broad-range PCR followed by cycle sequencing for direct detection of pathogens in appropriate clinical samples. The ability of proteomics (matrix-assisted desorption ionization-time of flight) versus 16S sequencing for bacterial identification and genotyping is compared. Finally, the potential for whole-genome analysis by next-generation sequencing (NGS) to replace 16S sequencing for routine diagnostic use is presented for several applications, including the barriers that must be overcome to fully implement newer genomic methods in clinical microbiology. A future challenge for large clinical, reference, and research laboratories, as well as for industry, will be the translation of vast amounts of accrued NGS microbial data into convenient algorithm testing schemes for various applications (i.e., microbial identification, genotyping, and metagenomics and microbiome analyses) so that clinically relevant information can be reported to physicians in a format that is understood and actionable. These challenges will not be faced by clinical microbiologists alone but by every scientist involved in a domain where natural diversity of genes and gene sequences plays a critical role in disease, health, pathogenicity, epidemiology, and other aspects of life-forms. Overcoming these challenges will require global multidisciplinary efforts across fields that do not normally interact with the clinical arena to make vast amounts of sequencing data clinically interpretable and actionable at the bedside.

Clinical application of 16S rRNA sequencing analysis for diagnosis of spontaneous bacterial peritonitis and evaluation of antibiotic efficacy

AIM: To explore a new method of polymerase chain reaction (PCR) based on 16S rRNA gene of bacteria with two different pairs of primers (used alone or in combination) and alignment sequencing, to improve the detection rate of bacteria in ascitic fluid and evaluate its clinical value in bacterial species identification and evaluation of antibiotic efficacy.

METHODS: Blood and ascitic fluid samples were collected from 77 spontaneous bacterial peritonitis (SBP) (n = 61) cirrhotic patients with ascites. Bacterial culture of ascitic fluid was conducted and the results were used as the gold standard in this study. Bacterial DNA in ascitic fluid was detected by PCR, based on the 16S rRNA gene, with two different pairs primers (MSQ-F/MSQ-R, 27F/1492R) and alignment sequencing assay. The results of bacterial species and positive rate between PCR method and bacterial culture of ascitic fluid were compared, and the results of bacterial species identified with different pair primers were also compared.

RESULTS: For products amplified with primers MSQ-F/MSQ-R, the coincidence rate of bacterial genus identification between PCR assay and ascitic fluid culture was 73.33% (11/15), and the coincidence rate of bacterial species identification was 66.67(10/15). For products amplified with primers27F/1492R, the coincidence rate of bacterial genus identification between PCR assay and ascitic fluid culture was 80.00% (12/15), and the coincidence rate of bacterial species identification was 73.33% (11/15). For products amplified with primers MSQ-F/MSQ-R combined with primers 27F/1492R, the coincidence rate of bacterial genus identification between PCR assay and ascitic fluid culture was 86.67% (13/15), and the coincidence rate of bacterial species identification was 80.00% (12/15). In five patients with SBP whose ascitic fluid culture was negative, PCR reaction was positive, which was consistent with clinical characteristics.

CONCLUSION: The method combining the two different pairs of primers for identifying bacteria based on the 16S rRNA gene and alignment sequencing has better identification ability and can evaluate antibiotic efficacy dynamically.

Benchmarking of methods for genomic taxonomy

One of the first issues that emerges when a prokaryotic organism of interest is encountered is the question of what it is--that is, which species it is. The 16S rRNA gene formed the basis of the first method for sequence-based taxonomy and has had a tremendous impact on the field of microbiology. Nevertheless, the method has been found to have a number of shortcomings. In the current study, we trained and benchmarked five methods for whole-genome sequence-based prokaryotic species identification on a common data set of complete genomes: (i) SpeciesFinder, which is based on the complete 16S rRNA gene; (ii) Reads2Type that searches for species-specific 50-mers in either the 16S rRNA gene or the gyrB gene (for the Enterobacteraceae family); (iii) the ribosomal multilocus sequence typing (rMLST) method that samples up to 53 ribosomal genes; (iv) TaxonomyFinder, which is based on species-specific functional protein domain profiles; and finally (v) KmerFinder, which examines the number of cooccurring k-mers (substrings of k nucleotides in DNA sequence data). The performances of the methods were subsequently evaluated on three data sets of short sequence reads or draft genomes from public databases. In total, the evaluation sets constituted sequence data from more than 11,000 isolates covering 159 genera and 243 species. Our results indicate that methods that sample only chromosomal, core genes have difficulties in distinguishing closely related species which only recently diverged. The KmerFinder method had the overall highest accuracy and correctly identified from 93% to 97% of the isolates in the evaluations sets.

Genome sequence analyses show that Neisseria oralis is the same species as 'Neisseria mucosa var. heidelbergensis'

Phylogenies generated from whole genome sequence (WGS) data provide definitive means of bacterial isolate characterization for typing and taxonomy. The species status of strains recently defined with conventional taxonomic approaches as representing Neisseria oralis was examined by the analysis of sequences derived from WGS data, specifically: (i) 53 Neisseria ribosomal protein subunit (rps) genes (ribosomal multi-locus sequence typing, rMLST); and (ii) 246 Neisseria core genes (core genome MLST, cgMLST). These data were compared with phylogenies derived from 16S and 23S rRNA gene sequences, demonstrating that the N. oralis strains were monophyletic with strains described previously as representing 'Neisseria mucosa var. heidelbergensis' and that this group was of equivalent taxonomic status to other well-described species of the genus Neisseria. Phylogenetic analyses also indicated that Neisseria sicca and Neisseria macacae should be considered the same species as Neisseria mucosa and that Neisseria flavescens should be considered the same species as Neisseria subflava. Analyses using rMLST showed that some strains currently defined as belonging to the genus Neisseria were more closely related to species belonging to other genera within the family; however, whole genome analysis of a more comprehensive selection of strains from within the family Neisseriaceae would be necessary to confirm this. We suggest that strains previously identified as representing 'N. mucosa var. heidelbergensis' and deposited in culture collections should be renamed N. oralis. Finally, one of the strains of N. oralis was able to ferment lactose, due to the presence of β-galactosidase and lactose permease genes, a characteristic previously thought to be unique to Neisseria lactamica, which therefore cannot be thought of as diagnostic for this species; however, the rMLST and cgMLST analyses confirm that N. oralis is most closely related to N. mucosa.

Evaluation of 16SpathDB 2.0, an automated 16S rRNA gene sequence database, using 689 complete bacterial genomes

Interpretation of 16S rRNA sequences is a difficult problem faced by clinical microbiologists and technicians. In this study, we evaluated the updated 16SpathDB 2.0 database, using 689 16S rRNA sequences from 689 complete genomes of medically important bacteria. Among these 689 16S rRNA sequences, none was wrongly identified, with 35.8% reported as a single bacterial species having >98% identity with the query sequence (category 1), 63.9% reported as more than 1 bacterial species having >98% identity with the query sequence (category 2), 0.3% reported to the genus level (category 3), and none reported as no match (category 4). For the 16S rRNA sequences of non-duplicated bacterial species reported as category 1 or 2, the percentage of bacterial species reported as category 1 was significantly higher for anaerobic Gram-positive/Gram-negative bacteria than aerobic/facultative anaerobic Gram-positive/Gram-negative bacteria. 16SpathDB 2.0 is a user-friendly and accurate database for 16S rRNA sequence interpretation in clinical laboratories.