Understanding the Promises and Hurdles of Metagenomic Next-Generation Sequencing as a Diagnostic Tool for Infectious Diseases

Abstract

Agnostic metagenomic next-generation sequencing (mNGS) has emerged as a promising single, universal pathogen detection method for infectious disease diagnostics. This methodology allows for identification and genomic characterization of bacteria, fungi, parasites, and viruses without the need for a priori knowledge of a specific pathogen directly from clinical specimens. Although there are increasing reports of mNGS successes, several hurdles need to be addressed, such as differentiation of colonization from infection, extraneous sources of nucleic acid, method standardization, and data storage, protection, analysis, and interpretation. As more commercial and clinical microbiology laboratories develop mNGS assays, it is important for treating practitioners to understand both the power and limitations of this method as a diagnostic tool for infectious diseases.

Figure 1.

Summary of the traditional timeline and workflow in diagnostic medical microbiology laboratories and the future state with the incorporation of metagenomic next-generation sequencing (mNGS) methodologies. Current organism detection techniques (orange), identification (yellow), antimicrobial susceptibility testing (green), and strain typing (purple) can take up to a week or longer from specimen collection (blue) to strain typing results. mNGS has the capability of greatly reducing turnaround times and providing all the data summarized in current methods in a single modality (red) and could potentially provide all of these within 24–48 hours of specimen receipt. To date, the available evidence is poor to use antibiotic resistance gene detection to predict phenotypic antimicrobial susceptibility testing profiles for clinical care [16]. Abbreviations: AFB, acid-fast bacilli; cDNA, complementary DNA; CSF, cerebrospinal fluid; KOH, potassium hydroxide; MALDI-TOF MS, matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry; MLST, multilocus sequence typing; mNGS, metagenomic next-generation sequencing; PCR, polymerase chain reaction; PFGE, pulsed-field gel electrophoresis; 16S rDNA, 16S ribosomal DNA.

Figure 2.

An overview of the different applications of next-generation sequencing analysis. A, Whole-genome sequencing of a pure organism from cultured growth. B, Targeted amplification of 16S rDNA from a clinical specimen for bacterial profiling. C, Metagenomic next-generation sequencing from clinical specimens. The nucleic acid composition of the specimens includes host (black), microbiome and pathogen detection (blue, green, and red), and last, the introduction of contaminating nucleic acid (orange). Analysis of reads generally involves removing host DNA from microbial DNA. The host DNA reads can be used to study the host immune response. The microbial reads are analyzed to identify the composition and abundance of reads of organisms present. The study of RNA can allow for transcriptome-based analysis to identify organisms that are transcriptionally active. Abbreviations: cDNA, complementary DNA; NGS, next-generation sequencing; NA, nucleic acid; 16S rDNA, 16S ribosomal DNA.

Figure 3.

Example of results output and bioinformatics analysis tools for metagenomic next-generation sequencing data. A, A simplified Kraken report showing the number and percentage of sequence reads and their alignment identification using Kraken for a cerebrospinal fluid (CSF) specimen from a patient diagnosed with JC virus encephalitis [17]. The overall Kraken report summarizing the data from the CSF specimen is >2000 line listings long (see Supplementary Data). Of note, Escherichia coli, Pseudomonasputida group, and Propionibacterium acnes (now Cutibacterium acnes) were considered reagent contaminants in this case as they were observed in the no-template control. B–D, Analysis modes of the Web-based Pavian program, a straightforward interface to analyze and compare complex metagenomics datasets. B, The number of sequence reads matching each taxa of interest are shown for the sample. Of note, almost all the virus reads align to JC polyomavirus. C, A heat map approach showing the percentage of microbially matched reads across multiple samples allowing for sample comparison. D, An interactive alignment tool showing the fold coverage of the reads over the whole JC virus genome [29].