Improved diagnosis of viral encephalitis in adult and pediatric hematological patients using viral metagenomics

Carbo, Ellen C.; Buddingh, Emilie P.; Karelioti, Evita; Sidorov, Igor A.; Feltkamp, Mariet C.W.; Borne, Peter A. von dem; Verschuuren, Jan; Kroes, Aloys C.M.; Claas, Eric C. J.; Vries, Jutte J.C. de

doi:10.1016/j.jcv.2020.104566

Cited by 41 publications

(44 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Genome coverage and depth was not always taken into account by the participating laboratories, however can be an effective parameter to distinguish between (PCR-)contaminants, often indicated by high depth at a small (PCR amplicon) region of the genome, and true positives [21,55]. In five of participating laboratories a cut-off of one single read was chosen for defining a positive mNGS result.…”

Section: Discussionmentioning

confidence: 99%

“…The other samples were spiked with Equine Arteritis Virus (EAV) and Phocid Herpes Virus (PhHV) internal controls preceding total nucleic acid extraction using the MagNAPure 96 DNA and Viral NA Small Volume Kit (Roche Diagnostics, Almere, the Netherlands) and sequenced on Illumina NextSeq500 (respiratory samples) or Nova-Seq6000 (CSF samples, plasma) instruments using 150 bp paired-end runs after library preparation using New England BioLabs' NEBNext Ultra Directional RNA Library preparation kit for Illumina with in-house adaptations in order to enable simultaneous detection of both DNA and RNA viruses, at the Leiden University Medical Center (LUMC) [4,20]. Three of the CSF samples were sequenced after enrichment using capture probes targeting vertebrate viruses [21]. Human reads from the output FASTQ files were removed after mapping them to human reference genome GRCh38 [22] with Bowtie2 version 2.3.4 [23] before the datasets were uploaded to various data sharing platforms (see below).…”

Section: Datasetsmentioning

confidence: 99%

“…The datasets were analysed in a blinded fashion by the participants, with the (viral) metagenomic pipelines and classification tools (Fig. 1 and Table 1) used at their diagnostic laboratories: Centrifuge [25], DAMIAN [26,27], DIAMOND [28], DNASTAR [29], FEVIR [30], Genome Detective [31], Jovian [32], MetaMIC [33], MetaMix [34,35], One Codex [36], RIEMS [37,38], Taxonomer [39], and VirMet [40]. DAMIAN was run by two participants in combination with a different database (pipeline A and B), and one participant run both Centrifuge and GenomeDetective.…”

Section: Bioinformatic Pipelinesmentioning

confidence: 99%

See 2 more Smart Citations

Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples

Vries

Brown

Fischer

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Metagenomic sequencing is increasingly being used in clinical settings for difficult to diagnose cases. The performance of viral metagenomic protocols relies to a large extent on the bioinformatic analysis. In this study, the European Society for Clinical Virology (ESCV) Network on NGS (ENNGS) initiated a benchmark of metagenomic pipelines currently used in clinical virological laboratories. Methods Metagenomic datasets from 13 clinical samples from patients with encephalitis or viral respiratory infections characterized by PCR were selected. The datasets were analysed with 13 different pipelines currently used in virological diagnostic laboratories of participating ENNGS members. The pipelines and classification tools were: Centrifuge, DAMIAN, DIAMOND, DNASTAR, FEVIR, Genome Detective, Jovian, MetaMIC, MetaMix, One Codex, RIEMS, VirMet, and Taxonomer. Performance, characteristics, clinical use, and user-friendliness of these pipelines were analysed. Results Overall, viral pathogens with high loads were detected by all the evaluated metagenomic pipelines. In contrast, lower abundance pathogens and mixed infections were only detected by 3/13 pipelines, namely DNASTAR, FEVIR, and MetaMix. Overall sensitivity ranged from 80% (10/13) to 100% (13/13 datasets). Overall positive predictive value ranged from 71-100%. The majority of the pipelines classified sequences based on nucleotide similarity (8/13), only a minority used amino acid similarity, and 6 of the 13 pipelines assembled sequences de novo. No clear differences in performance were detected that correlated with these classification approaches. Read counts of target viruses varied between the pipelines over a range of 2-3 log, indicating differences in limit of detection. Conclusion A wide variety of viral metagenomic pipelines is currently used in the participating clinical diagnostic laboratories. Detection of low abundant viral pathogens and mixed infections remains a challenge, implicating the need for standardization and validation of metagenomic analysis for clinical diagnostic use. Future studies should address the selective effects due to the choice of different reference viral databases.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Bioinformatic Pipelinesmentioning

confidence: 99%

See 1 more Smart Citation

Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples

Vries

Brown

Fischer

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Metagenomic next-generation sequencing (mNGS) is increasingly being applied for the identification of pathogens in undiagnosed cases suspected of infection [2][3][4]. Quantification of viral loads utilising mNGS remains a challenge [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Longitudinal Monitoring of DNA Viral Loads in Transplant Patients Using Quantitative Metagenomic Next-Generation Sequencing

et al. 2022

Self Cite

View full text Add to dashboard Cite

Introduction: Immunocompromised patients are prone to reactivations and (re-)infections of multiple DNA viruses. Viral load monitoring by single-target quantitative PCRs (qPCR) is the current cornerstone for virus quantification. In this study, a metagenomic next-generation sequencing (mNGS) approach was used for the identification and load monitoring of transplantation-related DNA viruses. Methods: Longitudinal plasma samples from six patients that were qPCR-positive for cytomegalovirus (CMV), Epstein-Barr virus (EBV), BK polyomavirus (BKV), adenovirus (ADV), parvovirus B19 (B19V), and torque teno-virus (TTV) were sequenced using the quantitative metagenomic Galileo Viral Panel Solution (Arc Bio, LLC, Cambridge, MA, USA) reagents and bioinformatics pipeline combination. Qualitative and quantitative performance was analysed with a focus on viral load ranges relevant for clinical decision making. Results: All pathogens identified by qPCR were also identified by mNGS. BKV, CMV, and HHV6B were additionally detected by mNGS, and could be confirmed by qPCR or auxiliary bioinformatic analysis. Viral loads determined by mNGS correlated with the qPCR results, with inter-method differences in viral load per virus ranging from 0.19 log10 IU/mL for EBV to 0.90 log10 copies/mL for ADV. TTV, analysed by mNGS in a semi-quantitative way, demonstrated a mean difference of 3.0 log10 copies/mL. Trends over time in viral load determined by mNGS and qPCR were comparable, and clinical thresholds for initiation of treatment were equally identified by mNGS. Conclusions: The Galileo Viral Panel for quantitative mNGS performed comparably to qPCR concerning detection and viral load determination, within clinically relevant ranges of patient management algorithms.

show abstract

“…Metagenomic next-generation sequencing (mNGS) is increasingly being applied for the identification of pathogens in undiagnosed cases suspected of an infectious disease [2][3] [4]. Quantification of viral loads by means of mNGS remains a challenge [5][6][7] [8].…”

Section: Introductionmentioning

confidence: 99%

Longitudinal monitoring of DNA viral loads in transplant patients using quantitative metagenomic next-generation sequencing

Carbo

Russcher

Kraakman

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Introduction Immunocompromised patients are prone to reactivations of multiple latent DNA viruses. Viral load monitoring by single-target quantitative PCRs (qPCR) is the current cornerstone for virus quantification. In this study, a metagenomic next-generation sequencing (mNGS) approach was used for identification and load monitoring of transplantation-related DNA viruses. Methods Longitudinal plasma samples from six patients that were qPCR-positive for cytomegalovirus (CMV), Epstein-Barr virus (EBV), BK polyoma virus (BKV, adenovirus (ADV), parvovirus B19 (B19V), and torque teno-virus (TTV) were sequenced using the quantitative metagenomic Galileo Viral Panel Solution (Arc Bio, LLC) reagents and bioinformatics pipeline combination. Qualitative and quantitative performance was analysed with focus on viral load ranges relevant for clinical decision-making. Results All pathogens identified by qPCR were also identified by mNGS. In addition, BKV, CMV, and HHV6B were detected by mNGS which were not ordered initially but could be confirmed by qPCR. Viral loads determined by mNGS correlated with the qPCR results, with inter-method differences in viral load per virus ranging from 0.19 log10 IU/ml for EBV to 0.90 log10 copies/ml for ADV. TTV, analysed by mNGS in a semi-quantitative way, showed a mean difference of 3.0 log10 copies/ml. Trends over time in viral load determined by mNGS and qPCR were comparable, and clinical thresholds for initiation of treatment were equally indicated by mNGS. Conclusion The Galileo Viral Panel for quantitative mNGS performed comparable to qPCR with regard to detection and viral load determination, within clinically relevant ranges of patient management algorithms.

show abstract

Improved diagnosis of viral encephalitis in adult and pediatric hematological patients using viral metagenomics

Cited by 41 publications

References 54 publications

Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples

Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples

Longitudinal Monitoring of DNA Viral Loads in Transplant Patients Using Quantitative Metagenomic Next-Generation Sequencing

Longitudinal monitoring of DNA viral loads in transplant patients using quantitative metagenomic next-generation sequencing

Contact Info

Product

Resources

About