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

Abstract

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.

Keywords: clinical application; infectious disease; metagenomics; next‐generation sequencing.

Figure 1

mNGS workflow in clinical application. This workflow consists of eight components. 1) Clinical evaluation: the test is suitable for patients with infectious diseases. 2) Sample collection: collecting samples from the primary site of infection can greatly increase the detection rate. 3) Sample preprocessing: the pretreatment methods for different types of samples are different, sputum needs liquefaction treatment, FFPE samples are dewaxed, and tissue needs homogenate. The percentage of human DNA in samples can be reduced using methods such as filtration, differential centrifugation, DNA enzymatic hydrolysis, and methylation reagent treatment. 4) Nucleic acid extraction: there are differences between DNA and RNA extraction. 5) Library preparation: library construction method is selected according to the sequencing platform and purpose. 6) Sequencing: at present, the mainstream second‐generation sequencing platforms are produced by Illumina and BGI. 7) Bioinformatic analysis: based on the analysis of the raw data, the information of species and antibiotic resistance genes in the samples were obtained. 8) Report: the possible pathogens were screened out according to the analysis results.

Figure 2

The bioinformatics analysis process starts with the fastq date, including the removal of low‐quality and low‐complex sequences, host and engineering bacteria sequences, and the identification of pathogens. Samples qualified for sequencing (Q30 qualified), the proportion of removing low‐quality and low‐complex sequences is about 5%, which accounts for about 20% of the whole process time. Removal of hosts, engineering bacteria, plasmids, different types of samples and different treatment methods, and the host proportion varies greatly, between 40% and 99%; this step accounts for about half of the whole process time. For taxonomic classification, different alignment software is quite different, such as k‐mer algorithm, there are 30% to 80% of the sequences, which can be assigned to microbes. The proportion of unclassified sequences is high, so can choose to compare with NR database to identify distant sequences. This step accounts for about 30%.