Dataset Information

Metagenomic Sequencing of an Echovirus 30 Genome From Cerebrospinal Fluid of a Patient With Aseptic Meningitis and Orchitis.

ABSTRACT: Enteroviruses cause a wide spectrum of clinical disease. In this study, we describe the case of a young man with orchitis and aseptic meningitis who was diagnosed with enterovirus infection. Using unbiased "metagenomic" massively parallel sequencing, we assembled a near-complete viral genome, the first use of this method for full-genome viral sequencing from cerebrospinal fluid. We found that the genome belonged to the subgroup echovirus 30, which is a common cause of aseptic meningitis but has not been previously reported to cause orchitis.

SUBMITTER: Piantadosi A

PROVIDER: S-EPMC5534216 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Metagenomic Sequencing of an Echovirus 30 Genome From Cerebrospinal Fluid of a Patient With Aseptic Meningitis and Orchitis.

Piantadosi Anne A Mukerji Shibani S SS Chitneni Pooja P Cho Tracey A TA Cosimi Lisa A LA Hung Deborah T DT Goldberg Marcia B MB Sabeti Pardis C PC Kuritzkes Daniel R DR Grad Yonatan H YH

Open forum infectious diseases 20170728 3

Enteroviruses cause a wide spectrum of clinical disease. In this study, we describe the case of a young man with orchitis and aseptic meningitis who was diagnosed with enterovirus infection. Using unbiased "metagenomic" massively parallel sequencing, we assembled a near-complete viral genome, the first use of this method for full-genome viral sequencing from cerebrospinal fluid. We found that the genome belonged to the subgroup echovirus 30, which is a common cause of aseptic meningitis but has ...[more]

PMID: 28761901

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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.

Dataset Information

Metagenomic Sequencing of an Echovirus 30 Genome From Cerebrospinal Fluid of a Patient With Aseptic Meningitis and Orchitis.

Publications

Metagenomic Sequencing of an Echovirus 30 Genome From Cerebrospinal Fluid of a Patient With Aseptic Meningitis and Orchitis.

Similar Datasets

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