Dataset Information

Viral Metagenomics on Cerebrospinal Fluid.

ABSTRACT: Identifying the causative pathogen in central nervous system (CNS) infections is crucial for patient management and prognosis. Many viruses can cause CNS infections, yet screening for each individually is costly and time-consuming. Most metagenomic assays can theoretically detect all pathogens, but often fail to detect viruses because of their small genome and low viral load. Viral metagenomics overcomes this by enrichment of the viral genomic content in a sample. VIDISCA-NGS is one of the available workflows for viral metagenomics, which requires only a small input volume and allows multiplexing of multiple samples per run. The performance of VIDISCA-NGS was tested on 45 cerebrospinal fluid (CSF) samples from patients with suspected CNS infections in which a virus was identified and quantified by polymerase chain reaction. Eighteen were positive for an RNA virus, and 34 for a herpesvirus. VIDISCA-NGS detected all RNA viruses with a viral load >2 × 10⁴ RNA copies/mL (n = 6) and 8 of 12 of the remaining low load samples. Only one herpesvirus was identified by VIDISCA-NGS, however, when withholding a DNase treatment, 11 of 18 samples with a herpesvirus load >10⁴ DNA copies/mL were detected. Our results indicate that VIDISCA-NGS has the capacity to detect low load RNA viruses in CSF. Herpesvirus DNA in clinical samples is probably non-encapsidated and therefore difficult to detect by VIDISCA-NGS.

SUBMITTER: Edridge AWD

PROVIDER: S-EPMC6562652 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Viral Metagenomics on Cerebrospinal Fluid.

Edridge Arthur W D AWD Deijs Martin M van Zeggeren Ingeborg E IE Kinsella Cormac M CM Jebbink Maarten F MF Bakker Margreet M van de Beek Diederik D Brouwer Matthijs C MC van der Hoek Lia L

Genes 20190430 5

Identifying the causative pathogen in central nervous system (CNS) infections is crucial for patient management and prognosis. Many viruses can cause CNS infections, yet screening for each individually is costly and time-consuming. Most metagenomic assays can theoretically detect all pathogens, but often fail to detect viruses because of their small genome and low viral load. Viral metagenomics overcomes this by enrichment of the viral genomic content in a sample. VIDISCA-NGS is one of the avail ...[more]

PMID: 31052348

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Project description:Differential diagnosis of bacterial meningitis (BM) and viral meningitis (VM) is a critical clinical challenge, as the early and accurate identification of the causative agent determines the appropriate treatment regimen and markedly improves patient outcomes. Clinical and experimental studies have demonstrated that the pathogen and the host immune response contribute to mortality and neurological sequelae. As BM is associated with the activation of an inflammatory cascade, the patterns of pro- and anti-inflammatory cytokines/chemokines (CTs/CKs) present in the cerebrospinal fluid (CSF) in response to the immune assault may be useful as sensitive markers for differentiating BM from VM. In the present study, the ability of CTs/CKs in the CSF to differentiate between BM and VM was investigated. For this, biochemical markers and CT/CK profiles were analysed in 145 CSF samples, divided into three groups: BM (n=61), VM (n=58) and the control group (C; n=26) comprising patients with meningism. The CSF concentrations of monocyte chemoattractant protein-1, interleukin (IL)-8, IL-1β, IL-6, macrophage inflammatory protein-1α (MIP-1α), epithelial-neutrophil activating peptide, IL-10, tumour necrosis factor-α (TNF-α), proteins and white blood cells were significantly higher and the CSF glucose level was significantly lower in the BM group compared with the VM and C groups (P<0.01). Correlation analysis identified 28 significant correlations between various CTs/CKs in the BM group (P<0.01), with the strongest positive correlations being for TNF-α/IL-6 (r=0.75), TNF-α/MIP-1α (r=0.69), TNF-α/IL-1β (r=0.64) and IL-1β/MIP-1α (r=0.64). To identify the optimum CT/CK patterns for predicting and classifying BM and VM, a dataset of 119 BM and VM samples was divided into training (n=90) and testing (n=29) subsets for use as input for a Random Forest (RF) machine learning algorithm. For the 29 test samples (15 BM and 14 VM), the RF algorithm correctly classified 28 samples, with 92% sensitivity and 93% specificity. The results show that the patterns of CT/CK levels in the CSF can be used to aid discrimination of BM and VM.

Dataset Information

Viral Metagenomics on Cerebrospinal Fluid.

Publications

Viral Metagenomics on Cerebrospinal Fluid.

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