The application of sequencing technology is shifting from research to clinical laboratories owing to rapid technological developments and substantially reduced costs. However, although thousands of microorganisms are known to infect humans, identification of the etiological agents for many diseases remains challenging as only a small proportion of pathogens are identifiable by the current diagnostic methods. These challenges are compounded by the emergence of new pathogens. Hence, metagenomic next-generation sequencing (mNGS), an agnostic, unbiased, and comprehensive method for detection, and taxonomic characterization of microorganisms, has become an attractive strategy. Although many studies, and cases reports, have confirmed the success of mNGS in improving the diagnosis, treatment, and tracking of infectious diseases, several hurdles must still be overcome. It is, therefore, imperative that practitioners and clinicians understand both the benefits and limitations of mNGS when applying it to clinical practice. Interestingly, the emerging third-generation sequencing technologies may partially offset the disadvantages of mNGS. In this review, mainly: a) the history of sequencing technology; b) various NGS technologies, common platforms, and workflows for clinical applications; c) the application of NGS in pathogen identification; d) the global expert consensus on NGS-related methods in clinical applications; and e) challenges associated with diagnostic metagenomics are described.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/smtd.202000792

High‐Throughput Metagenomics for Identification of Pathogens in the Clinical Settings

Over the past two decades, the diagnosis rate for patients with encephalitis has remained poor despite advances in pathogen-specific testing such as PCR and antigen assays. Metagenomic next-generation sequencing (mNGS) of RNA and DNA extracted from cerebrospinal fluid and brain tissue now offers another strategy for diagnosing neurological infections. Given that mNGS simultaneously assays for a wide range of infectious agents in an unbiased manner, it can identify pathogens that were not part of a neurologist's initial differential diagnosis either because of the rarity of the infection, because the microorganism has not been previously associated with a clinical phenotype or because it is a newly discovered organism. This Review discusses the technical advantages and pitfalls of cerebrospinal fluid mNGS in the context of patients with neuroinflammatory syndromes, including encephalitis, meningitis and myelitis. We also speculate on how mNGS testing potentially fits into current diagnostic testing algorithms given data on mNGS test performance, cost and turnaround time. Finally, the Review highlights future directions for mNGS technology and other hypothesis-free testing methodologies that are in development.

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Metagenomics for neurological infections — expanding our imagination

Next-generation sequencing of the cerebrospinal fluid in the diagnosis of neurobrucellosis

Objectives We have developed a portable system for the rapid determination of bacterial composition for the diagnosis of infectious diseases. Our system comprises of a nanopore technology-based sequencer, MinION, and two laptop computers. To examine the accuracy and time efficiency of our system, we provided a proof-of-concept for the detection of the causative bacteria of 11 meningitis patients in Zambia. Methods We extracted DNA from cerebrospinal fluid samples of each patient and amplified the 16S rRNA gene regions. The sequencing library was prepared, and the sequenced reads were simultaneously processed for bacterial composition determination using the minimap2 software and the representative prokaryote genomes. Results The sequencing results of four of the six culture-positive samples were consistent with those of conventional culture-based methods. The dominant bacterial species in each of these samples were identified from the sequencing data within only 3 min. Although the major bacterial species were also detected from the other two culture-positive samples and five culture-negative samples, their presence could not be confirmed. Moreover, as a whole, although the number of sequencing reads obtained within a short sequencing run was small, there was no change in the major bacterial species over time with prolonged sequencing. In addition, the processing time strongly correlated with the number of sequencing reads used for the analysis. Conclusion Our results suggest that time-effective analysis could be achieved by determining the number of sequencing reads required for the rapid diagnosis of infectious bacterial species depending on the complexity of bacterial species in a sample.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cti2.1087

Rapid sequencing-based diagnosis of infectious bacterial species from meningitis patients in Zambia.

The Mitis group of streptococci includes an important human pathogen, Streptococcus pneumoniae (pneumococcus) and about 20 other related species with much lower pathogenicity. In clinical practice, some representatives of these species, especially Streptococcus pseudopneumoniae and Streptococcus mitis, are sometimes mistaken for S. pneumoniae based on the results of classical microbiological methods, such as optochin susceptibility and bile solubility. Several various molecular approaches that address the issue of correct identification of pneumococci and other Mitis streptococci have been proposed and are discussed in this review, including PCR- and gene sequencing-based tests as well as new developments in the genomic field that represents an important advance in our understanding of relationships within the Mitis group.

/pdf/identification-of-streptococcus-pneumoniae-and-other-mitis-mghd2w3lex.pdf

Identification of Streptococcus pneumoniae and other Mitis streptococci: importance of molecular methods

There is currently no research on the diagnostic value of metagenomic next-generation sequencing (mNGS) for a single pathogens in CSF. The aim of this study was to analyse the value of mNGS for identifying Streptococcus pneumoniae (S. pneumoniae) in paediatric bacterial meningitis. Bacterial meningitis (BM) cases from October 23, 2014, to December 31, 2016, and December 1, 2017, to July 31, 2018 at Beijing Children’s Hospital were reviewed. Clinical features and pathogens were analysed. We diagnosed 135 patients with BM in this study. A total of 43 S. pneumoniae were identified by combination methods. 26/135 (19.3%) patients had positive results in S. pneumoniae by blood and/or cerebrospinal fluid (CSF) culture. Alere BinaxNow®Streptococcus pneumoniae Antigen test was positive in 35/135(25.9%) cases. 32/135 (23.7%) S. pneumoniae were identified by mNGS. Six CSF samples were identified as S. pneumoniae only by mNGS technology. Taking culture as the gold standard, the sensitivity and specificity of mNGS for diagnosing S. pneumoniae meningitis were 73.1 and 88.1%, respectively. The positive predictive value (PPV) and negative predictive value (NPV) of diagnosing S. pneumoniae meningitis by mNGS were 59.4 and 93.2%, respectively. When comparison between mNGS and combined tests (culture and Alere BinaxNow®Streptococcus pneumoniae Antigen test), the sensitivity and specificity of mNGS for S. pneumoniae identification were 70.3 and 93.9%, the PPV and NPV in the identification of S. pneumoniae by mNGS were 81.4 and 89.3%, respectively. The difference in number of unique reads of S. pneumoniaein from CSF sample (  14 days from onset) was statistically significant (170.5 VS. 13, P = 0.019). The difference in the collected time of CSF for culture and mNGS was statistically significant (4 days VS. 14 days, P < 0.001). mNGS has high sensitivity and specificity for S. pneumoniae identification. The pathogen load (number of unique reads) of S. pneumonia is related to the CSF collection time. mNGS was less affected than culture by the use of antibiotics before CSF collection.

/pdf/the-diagnostic-value-of-metagenomic-next-generation-yxua2jdl3k.pdf

Ao Shen

Papers

The diagnostic value of metagenomic next-generation sequencing for identifying Streptococcus pneumoniae in paediatric bacterial meningitis.