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BMScan: using whole genome similarity to rapidly and accurately identify bacterial meningitis causing species.

ABSTRACT: BACKGROUND:Bacterial meningitis is a life-threatening infection that remains a public health concern. Bacterial meningitis is commonly caused by the following species: Neisseria meningitidis, Streptococcus pneumoniae, Listeria monocytogenes, Haemophilus influenzae and Escherichia coli. Here, we describe BMScan (Bacterial Meningitis Scan), a whole-genome analysis tool for the species identification of bacterial meningitis-causing and closely-related pathogens, an essential step for case management and disease surveillance. BMScan relies on a reference collection that contains genomes for 17 focal species to scan against to identify a given species. We established this reference collection by supplementing publically available genomes from RefSeq with genomes from the isolate collections of the Centers for Disease Control Bacterial Meningitis Laboratory and the Minnesota Department of Health Public Health Laboratory, and then filtered them down to a representative set of genomes which capture the diversity for each species. Using this reference collection, we evaluated two genomic comparison algorithms, Mash and Average Nucleotide Identity, for their ability to accurately and rapidly identify our focal species. RESULTS:We found that the results of Mash were strongly correlated with the results of ANI for species identification, while providing a significant reduction in run-time. This drastic difference in run-time enabled the rapid scanning of large reference genome collections, which, when combined with species-specific threshold values, facilitated the development of BMScan. Using a validation set of 15,503 genomes of our species of interest, BMScan accurately identified 99.97% of the species within 16 min 47 s. CONCLUSIONS:Identification of the bacterial meningitis pathogenic species is a critical step for case confirmation and further strain characterization. BMScan employs species-specific thresholds for previously-validated, genome-wide similarity statistics compiled from a curated reference genome collection to rapidly and accurately identify the species of uncharacterized bacterial meningitis pathogens and closely related pathogens. BMScan will facilitate the transition in public health laboratories from traditional phenotypic detection methods to whole genome sequencing based methods for species identification.

SUBMITTER: Topaz N

PROVIDER: S-EPMC6094466 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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BMScan: using whole genome similarity to rapidly and accurately identify bacterial meningitis causing species.

Topaz Nadav N Boxrud Dave D Retchless Adam C AC Nichols Megan M Chang How-Yi HY Hu Fang F Wang Xin X

BMC infectious diseases 20180815 1

<h4>Background</h4>Bacterial meningitis is a life-threatening infection that remains a public health concern. Bacterial meningitis is commonly caused by the following species: Neisseria meningitidis, Streptococcus pneumoniae, Listeria monocytogenes, Haemophilus influenzae and Escherichia coli. Here, we describe BMScan (Bacterial Meningitis Scan), a whole-genome analysis tool for the species identification of bacterial meningitis-causing and closely-related pathogens, an essential step for case m ...[more]

PMID: 30111301

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Project description:BackgroundThe Streptococcus genus is relevant to both public health and food safety because of its ability to cause pathogenic infections. It is well-represented (>100 genomes) in publicly available databases. Streptococci are ubiquitous, with multiple sources of isolation, from human pathogens to dairy products. The Streptococcus genus has traditionally been classified by morphology, serum types, the 16S ribosomal RNA (rRNA) gene, and multi-locus sequence types subject to in-depth comparative genomic analysis.MethodsCore and pan-genomes described the genomic diversity of 108 strains belonging to 16 Streptococcus species. The core genome nucleotide diversity was calculated and compared to phylogenomic distances within the genus Streptococcus. The core genome was also used as a resource to recruit metagenomic fragment reads from streptococci dominated environments. A conventional 16S rRNA gene phylogeny reconstruction was used as a reference to compare the resulting dendrograms of average nucleotide identity (ANI) and genome similarity score (GSS) dendrograms.ResultsThe core genome, in this work, consists of 404 proteins that are shared by all 108 Streptococcus. The average identity of the pairwise compared core proteins decreases proportionally to GSS lower scores, across species. The GSS dendrogram recovers most of the clades in the 16S rRNA gene phylogeny while distinguishing between 16S polytomies (unresolved nodes). The GSS is a distance metric that can reflect evolutionary history comparing orthologous proteins. Additionally, GSS resulted in the most useful metric for genus and species comparisons, where ANI metrics failed due to false positives when comparing different species.DiscussionUnderstanding of genomic variability and species relatedness is the goal of tools like GSS, which makes use of the maximum pairwise shared orthologous sequences for its calculation. It allows for long evolutionary distances (above species) to be included because of the use of amino acid alignment scores, rather than nucleotides, and normalizing by positive matches. Newly sequenced species and strains could be easily placed into GSS dendrograms to infer overall genomic relatedness. The GSS is not restricted to ubiquitous conservancy of gene features; thus, it reflects the mosaic-structure and dynamism of gene acquisition and loss in bacterial genomes.

Project description:PurposeThe application of metagenomic next-generation sequencing (mNGS) in the diagnosis of tuberculous meningitis (TBM) remains poorly characterized. Here, we retrospectively analyzed data from patients with TBM who had taken both mNGS and conventional tests including culture of Mycobacterium tuberculosis (MTB), polymerase chain reaction (PCR) and acid-fast bacillus (AFB) stain, and the sensitivity and specificity of these methods were compared.MethodsWe retrospectively recruited TBM patients admitted to the hospital between December 2015 and October 2018. The first collection of cerebrospinal fluid (CSF) samples underwent both mNGS and conventional tests. In addition, patients with bacterial/cryptococcal meningitis or viral meningoencephalitis were mNGS positive controls, and a patient with auto-immune encephalitis was an mNGS negative control.ResultsTwenty three TBM patients were classified as 12 definite and 11 clinical diagnoses, which were based on clinical manifestations, pathogen evidence, CSF parameters, brain imaging, and treatment response. The mNGS method identified sequences of Mycobacterium tuberculosis complex (MBTC) from 18 samples (18/23, 78.26%). In patients with definite TBM, the sensitivity of mNGS, AFB, PCR, and culture to detect MTB in the first CSF samples were 66.67, 33.33, 25, and 8.33%, respectively. The specificity of each method was 100%. Among the four negative mNGS cases (4/23, 17.39%), three turned out positive by repeated AFB stain. The agreement of mNGS with the total of conventional methods was 44.44% (8/18). Combination of mNGS and conventional methods increased the detection rate to 95.65%. One patient was diagnosed as complex of TBM and cryptococcal meningitis, in which AFB stain and cryptococcal antigen enzyme immunoassay were positive and the DNA of Cryptococcus neoformans was detected by mNGS.ConclusionOur study indicates that mNGS is an alternative method to detect the presence of mycobacterial DNA in CSF samples from patients with TBM and deserves to be applied as a front-line CSF test.

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BMScan: using whole genome similarity to rapidly and accurately identify bacterial meningitis causing species.

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BMScan: using whole genome similarity to rapidly and accurately identify bacterial meningitis causing species.

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