Next-generation sequencing in the diagnosis of viral encephalitis: sensitivity and clinical limitations

Abstract

Identification of pathogens causing viral encephalitis remains challenging, and in over 50% of cases the etiologic factor remains undetermined. Next-generation sequencing (NGS) based metagenomics has been successfully used to detect novel and rare infections, but its value for routine diagnosis of encephalitis remains unclear. The aim of the present study was to determine the sensitivity of shotgun metagenomic sequencing protocols, which include preamplification, and testing it against cerebrospinal fluid (CSF) samples from encephalitis patients. For sensitivity testing HIV and HBV positive sera were serially diluted in CSF from an uninfected patient. NGS repeatedly detected HIV and HBV sequences present at concentrations from 105 to 102 and from 105 to 10 viral copies/reaction, respectively. However, when the same protocols were applied to RT-PCR/PCR positive CSF samples from 6 patients with enteroviral encephalitis (median viral load 47 copies/ml) and 15 patients with HSV, CMV or VZV encephalitis (median viral load 148 copies/ml), only 7 (28.6%) were identified as positive. In conclusions, while NGS has the advantage of being able to identify a wide range of potential pathogens it seems to be less sensitive compared to the standard amplification-based assays in the diagnosis of encephalitis, where low viral loads are common.

Clinical usefulness of metagenomic next-generation sequencing for the diagnosis of central nervous system infection in people living with HIV

“…Another 2 neurosyphilis cases were also missed by mNGS in the current study, showing mNGS may be not suitable in patients for whom the suspected pathogen is typically diagnosed by serology (Ramachandran and Wilson, 2020). Similarly, mNGS has also been recently shown to be less sensitive than the conventional PCR assays in the diagnosis of viral encephalitis when low viral loads are common (Perlejewski et al, 2020). Therefore, negative mNGS results should be interpreted with caution if suspected pathogens are of low abundance or even absent in CSF (Wilson et al, 2019).…”

Viral RNA Metagenomics of Hyalomma Ticks Collected from Dromedary Camels in Makkah Province, Saudi Arabia

“…Here, we report the presence of AHFV by qPCR in two pools, one from Al-Taif city and another from the Alkhurma district, but the results could not be confirmed with NGS, most likely due to the low copy number of the virus genome. Despite the robustness of NGS for the identification of a plethora of pathogens, it is less sensitive than qPCR for the detection of viruses [ 50 ]. Of the family Flaviviridae , pestivirus sequences, similar to Bole tick virus 4 and TBOV, were identified recently in ticks from in Trinidad and Tobago [ 32 ].…”

Contamination Issue in Viral Metagenomics: Problems, Solutions, and Clinical Perspectives

“…Contaminants can easily dominate in low-biomass samples generating background noise that is much higher than true signal originating from the target virus ( Malboeuf et al, 2013 ; Salter et al, 2014 ). So far, a wide variety of environmental and clinical samples containing low viral biomasses have been studied with SM workflows including air ( Prussin et al, 2019 ), glacier ice ( Zhong et al, 2020 ), human skin ( Tirosh et al, 2018 ), nasal swabs ( Altan et al, 2019 ), and CSF ( Perlejewski et al, 2020b ; Perlejewski et al, 2020c ). Most widely used library preparation kits for sequencing require inputs as low as 1 ng of DNA (e.g., llumina Nextra XT), but this may still be unattainable for some low-biomass samples.…”

“…While SM is being used to characterize the virome using various workflows, it still faces numerous challenges, including the decision regarding best extraction and sequencing methods, the need for host genomic background depletion, the necessity of access to computational resources and highly specialized bioinformaticists, and providing relevant clinical data fast enough to be of clinical value ( Schlaberg et al, 2017 ; Boers et al, 2019 ). Overall, SM approach has allowed for comprehensive surveys of never-before-seen viral communities ( Moreno-Gallego et al, 2019 ; Waldvogel-Abramowski et al, 2019 ; Perlejewski et al, 2020b ). However, SM also detects external contaminant nucleic acids and cross-contaminations, which can affect the interpretation of microbiome data ( Xu et al, 2013 ; Laurence et al, 2014 ).…”