Metagenomic Next-Generation Sequencing for Pathogen Detection and Transcriptomic Analysis in Pediatric Central Nervous System Infections

Ramchandar, Nanda; Coufal, Nicole G.; Warden, Anna S.; Briggs, Benjamin; Schwarz, Toni M.; Czakó, Rita; Xie, Heng; Salais, Robert; Foley, Joseph M.; Clarke, Christina; Waldeman, Bryce; Enriquez, Claudia; Osborne, Stephanie; Arrieta, Antonio; Salyakina, Daria; Janvier, Michelin; Sendi, Prithvi; Totapally, Balagangadhar; Dimmock, David; Farnaes, Lauge

doi:10.1093/ofid/ofab104

Cited by 22 publications

(12 citation statements)

References 39 publications

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“…In-depth etiological analysis showed that the distribution of pathogenic bacteria and viruses detected was basically consistent with the epidemiology of pathogens in pediatric CNS infections ( 45 ). The development of the human immune system is a continuous process, potentially gaining peak function during adolescence ( 46 ), which makes young children at greater risk of developing infections than older children and adults and makes them more vulnerable to disseminated infections ( 47 , 48 ), including CNS infections.…”

Section: Discussionmentioning

confidence: 61%

Diagnosis of Neurological Infections in Pediatric Patients from Cell-Free DNA Specimens by Using Metagenomic Next-Generation Sequencing

et al. 2023

Microbiol Spectr

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Our study emphasized that cfDNA mNGS had overall superiority to conventional methods on causative pathogen detection in CNS-infected children, and it was even better than wcDNA mNGS. cfDNA mNGS detected a wide range of pathogens with a high total coincidence rate (67.5%) against clinical diagnosis. The best timing for cfDNA mNGS detection ranged from 1 to 6 days, rather than 0 days, after the start of empirical anti-infection therapy.

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Section: Discussionmentioning

confidence: 61%

Diagnosis of Neurological Infections in Pediatric Patients from Cell-Free DNA Specimens by Using Metagenomic Next-Generation Sequencing

et al. 2023

Microbiol Spectr

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“…Protein–protein interaction enrichment analysis also identified this GO term, suggesting that it is part of the host response to viral infection. Therefore, transcriptome analysis supports the results of metagenomic NGS and elucidates the host immune response [ 54 ]. These results may be useful for understanding the pathophysiology of various pediatric CNSIs.…”

Section: Discussionmentioning

confidence: 90%

Performance of Nanopore and Illumina Metagenomic Sequencing for Pathogen Detection and Transcriptome Analysis in Infantile Central Nervous System Infections

Horiba

Torii

Aizawa

et al. 2022

Open Forum Infectious Diseases

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Background Infantile central nervous system infections (CNSIs) can be life-threatening and cause severe sequelae. However, the causative microorganism remains unknown in > 40% of patients with aseptic infections. This study aimed to analyze metagenome for detection of pathogens, and transcriptome for host immune responses during infection, in a single cerebrospinal fluid (CSF) sample using two different next-generation sequencing (NGS) platforms, Nanopore and Illumina. Methods Twenty-eight CNSIs patients (<12 months) were enrolled, and 49 clinical samples (28 CSF and 21 blood) were collected. The DNA extracted from all 49 samples were sequenced using Illumina sequencer for the detection of pathogens. Extracted RNA were obtained in sufficient quantities from 23 CSF samples and these were subjected to sequencing on both Nanopore and Illumina platforms. Human-derived reads subtracted during pathogen detection were used for host transcriptomic analysis from both Nanopore and Illumina sequencing. Results RNA metagenomic sequencing using both sequencing platforms revealed putative viral pathogens in 10 cases. DNA sequencing using Illumina sequencer detected two pathogens. The results of Nanopore and Illumina RNA sequencing were consistent; however, the mapping coverage and depth to the detected pathogen genome of Nanopore RNA sequencing were greater than those of Illumina. Host transcriptomic analysis of Nanopore sequencing revealed highly expressed genes related to the antiviral roles of innate immunity from pathogen-identified cases. Conclusions The use of Nanopore RNA sequencing for metagenomic diagnostics of CSF samples should help to understand both pathogens and host immune responses of CNSI and could shed light on the pathogenesis of these infections.

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“…Of which, four studies were retrospective ( 15 , 16 , 18 , 20 ) and eight were prospective ( 9 , 17 , 19 , 21 – 25 ). Studies were conducted in the following countries: China ( 9 , 17 , 22 , 23 ), the United States ( 16 , 18 – 21 , 24 ), Canada ( 15 ), and the unknown ( 25 ). A total of 1,249 participants were analyzed in the 12 studies.…”

Section: Resultsmentioning

confidence: 99%

Metagenomics next-generation sequencing for the diagnosis of central nervous system infection: A systematic review and meta-analysis

Chen²,

Ouyang³

et al. 2022

Front. Neurol.

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ObjectiveIt is widely acknowledged that central nervous system (CNS) infection is a serious infectious disease accompanied by various complications. However, the accuracy of current detection methods is limited, leading to delayed diagnosis and treatment. In recent years, metagenomic next-generation sequencing (mNGS) has been increasingly adopted to improve the diagnostic yield. The present study sought to evaluate the value of mNGS in CNS infection diagnosis.MethodsFollowing the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2022 guidelines, we searched relevant articles published in seven databases, including PubMed, Web of Science, and Cochrane Library, published from January 2014 to January 2022. High-quality articles related to mNGS applications in the CNS infection diagnosis were included. The comparison between mNGS and the gold standard of CNS infection, such as culture, PCR or serology, and microscopy, was conducted to obtain true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values, which were extracted for sensitivity and specificity calculation.ResultsA total of 272 related studies were retrieved and strictly selected according to the inclusion and exclusion criteria. Finally, 12 studies were included for meta-analysis and the pooled sensitivity was 77% (95% CI: 70–82%, I2 = 39.69%) and specificity was 96% (95% CI: 93–98%, I2 = 72.07%). Although no significant heterogeneity in sensitivity was observed, a sub-group analysis was conducted based on the pathogen, region, age, and sample pretreatment method to ascertain potential confounders. The area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) of mNGS for CNS infection was 0.91 (95% CI: 0.88–0.93). Besides, Deek's Funnel Plot Asymmetry Test indicated no publication bias in the included studies (Figure 3, p > 0.05).ConclusionOverall, mNGS exhibits good sensitivity and specificity for diagnosing CNS infection and diagnostic performance during clinical application by assisting in identifying the pathogen. However, the efficacy remains inconsistent, warranting subsequent studies for further performance improvement during its clinical application.Study registration numberINPLASY202120002

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Metagenomic Next-Generation Sequencing for Pathogen Detection and Transcriptomic Analysis in Pediatric Central Nervous System Infections

Cited by 22 publications

References 39 publications

Diagnosis of Neurological Infections in Pediatric Patients from Cell-Free DNA Specimens by Using Metagenomic Next-Generation Sequencing

Diagnosis of Neurological Infections in Pediatric Patients from Cell-Free DNA Specimens by Using Metagenomic Next-Generation Sequencing

Performance of Nanopore and Illumina Metagenomic Sequencing for Pathogen Detection and Transcriptome Analysis in Infantile Central Nervous System Infections

Metagenomics next-generation sequencing for the diagnosis of central nervous system infection: A systematic review and meta-analysis

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