Metagenomic Pathogen Sequencing in Resource-Scarce Settings: Lessons Learned and the Road Ahead

Metagenomic Next-generation Sequencing in Patients With Infectious Meningoencephalitis: A Comprehensive Systematic Literature Review and Meta-analysis

Background

We aimed to assess the accuracy, clinical efficacy, and limitations of metagenomic next-generation sequencing (mNGS) for diagnosing infectious meningoencephalitis.

Methods

We performed a systematic literature review and meta-analysis of studies that evaluated the performance of mNGS to determine the cause of infectious meningoencephalitis. We explored PubMed, Cumulative Index to Nursing and Allied Health, Embase, Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and Web of Science up to 12 November 2024. To perform a meta-analysis, we calculated the pooled diagnostic odds ratio (DOR) for mNGS and for conventional microbiological tests (CMTs) compared to the clinical diagnosis.

Results

Thirty-four studies met the inclusion criteria, with mNGS-positive rates ranging from 43.5% to 93.5% for infectious meningoencephalitis. The meta-analysis included 23 studies with 1660 patients. The pooled sensitivity was 0.70 (95% confidence interval [CI], .67-.72), and its specificity was 0.93 (95% CI, .92-.94). The DOR for mNGS was 26.7 (95% CI, 10.4-68.8), compared to 12.2 (95% CI, 3.2-47.0) for CMTs. For tuberculosis meningoencephalitis, mNGS demonstrated a pooled sensitivity of 0.67 (95% CI, .61-.72) and specificity of 0.97 (95% CI, .95-.99), with a DOR of 43.5 (95% CI, 7.4-256.6).

Conclusions

Our review indicates that mNGS can be a valuable diagnostic tool for infectious meningoencephalitis, offering high sensitivity and specificity. mNGS's superior DOR compared to that of CMTs highlights its potential for more accurate diagnoses and targeted interventions. Further research is needed to optimize which patients and at what point in the diagnostic process mNGS should be used.

Skimming genomes for systematics and DNA barcodes of corals

Numerous genomic methods developed over the past two decades have enabled the discovery and extraction of orthologous loci to help resolve phylogenetic relationships across various taxa and scales. Genome skimming (or low-coverage genome sequencing) is a promising method to not only extract high-copy loci but also 100s to 1000s of phylogenetically informative nuclear loci (e.g., ultraconserved elements [UCEs] and exons) from contemporary and museum samples. The subphylum Anthozoa, including important ecosystem engineers (e.g., stony corals, black corals, anemones, and octocorals) in the marine environment, is in critical need of phylogenetic resolution and thus might benefit from a genome-skimming approach. We conducted genome skimming on 242 anthozoan corals collected from 1886 to 2022. Using existing target-capture baitsets, we bioinformatically obtained UCEs and exons from the genome-skimming data and incorporated them with data from previously published target-capture studies. The mean number of UCE and exon loci extracted from the genome skimming data was 1837 ± 662 SD for octocorals and 1379 ± 476 SD loci for hexacorals. Phylogenetic relationships were well resolved within each class. A mean of 1422 ± 720 loci was obtained from the historical specimens, with 1253 loci recovered from the oldest specimen collected in 1886. We also obtained partial to whole mitogenomes and nuclear rRNA genes from >95% of samples. Bioinformatically pulling UCEs, exons, mitochondrial genomes, and nuclear rRNA genes from genome skimming data is a viable and low-cost option for phylogenetic studies. This approach can be used to review and support taxonomic revisions and reconstruct evolutionary histories, including historical museum and type specimens.

Threat Net: A Metagenomic Surveillance Network for Biothreat Detection and Early Warning

Early detection of novel pathogens can prevent or substantially mitigate biological incidents, including pandemics. Metagenomic next-generation sequencing (mNGS) of symptomatic clinical samples may enable detection early enough to contain outbreaks, limit international spread, and expedite countermeasure development. In this article, we propose a clinical mNGS architecture we call "Threat Net," which focuses on the hospital emergency department as a high-yield surveillance location. We develop a susceptible-exposed-infected-removed (SEIR) simulation model to estimate the effectiveness of Threat Net in detecting novel respiratory pathogen outbreaks. Our analysis serves to quantify the value of routine clinical mNGS for respiratory pandemic detection by estimating the cost and epidemiological effectiveness at differing degrees of hospital coverage across the United States. We estimate that a biological threat detection network such as Threat Net could be deployed across hospitals covering 30% of the population in the United States. Threat Net would cost between $400 million and $800 million annually and have a 95% chance of detecting a novel respiratory pathogen with traits of SARS-CoV-2 after 10 emergency department presentations and 79 infections across the United States. Our analyses suggest that implementing Threat Net could help prevent or substantially mitigate the spread of a respiratory pandemic pathogen in the United States.

Clinical metagenomics-challenges and future prospects

Infections lacking precise diagnosis are often caused by a rare or uncharacterized pathogen, a combination of pathogens, or a known pathogen carrying undocumented or newly acquired genes. Despite medical advances in infectious disease diagnostics, many patients still experience mortality or long-term consequences due to undiagnosed or misdiagnosed infections. Thus, there is a need for an exhaustive and universal diagnostic strategy to reduce the fraction of undocumented infections. Compared to conventional diagnostics, metagenomic next-generation sequencing (mNGS) is a promising, culture-independent sequencing technology that is sensitive to detecting rare, novel, and unexpected pathogens with no preconception. Despite the fact that several studies and case reports have identified the effectiveness of mNGS in improving clinical diagnosis, there are obvious shortcomings in terms of sensitivity, specificity, costs, standardization of bioinformatic pipelines, and interpretation of findings that limit the integration of mNGS into clinical practice. Therefore, physicians must understand the potential benefits and drawbacks of mNGS when applying it to clinical practice. In this review, we will examine the current accomplishments, efficacy, and restrictions of mNGS in relation to conventional diagnostic methods. Furthermore, we will suggest potential approaches to enhance mNGS to its maximum capacity as a clinical diagnostic tool for identifying severe infections.

A case for investment in clinical metagenomics in low-income and middle-income countries

Clinical metagenomics is the diagnostic approach with the broadest capacity to detect both known and novel pathogens. Clinical metagenomics is costly to run and requires infrastructure, but the use of next-generation sequencing for SARS-CoV-2 molecular epidemiology in low-income and middle-income countries (LMICs) offers an opportunity to direct this infrastructure to the establishment of clinical metagenomics programmes. Local implementation of clinical metagenomics is important to create relevant systems and evaluate cost-effective methodologies for its use, as well as to ensure that reference databases and result interpretation tools are appropriate to local epidemiology. Rational implementation, based on the needs of LMICs and the available resources, could ultimately improve individual patient care in instances in which available diagnostics are inadequate and supplement emerging infectious disease surveillance systems to ensure the next pandemic pathogen is quickly identified.