Metagenomic Next-Generation Sequencing in Clinical Microbiology
2020
Abstract Identification of causative pathogens in infectious disease is a critical component of health care, as infectious diseases continue to be a leading cause of mortality and morbidity worldwide. In addition, the detection of drug resistance by traditional and novel antimicrobial susceptibility testing methods is becoming ever more important as antimicrobial resistance continues to emerge and spread. While culture-based methods are the current reference standard for identifying microbes, the time required to achieve results and the difficulty in culturing certain fastidious organisms have led to the development of multiple alternatives. Alternatives to culture, such as polymerase chain reaction (PCR) assays, serologic assays, matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS), and 16S rRNA gene sequencing have been used in clinical laboratories, and positive impacts on patient care have been documented. Nonetheless, such methods require either presumption about the type of microbes present in the sample or growth of the organism(s) before analysis. Subsequently, there are some documented limitations in the routine detection methods for clinically relevant organisms. In contrast to the routine alternatives listed above, modern nucleic acid sequencing platforms support sequencing of random DNA strands, an approach known as “shotgun” sequencing of the DNA, also known as metagenomic next-generation sequencing (mNGS) present in the sample. The mNGS approach offers an unbiased and hypothesis-free approach to pathogen identification with the future potential to (i) achieve results in 12 to 24 hours; (ii) avoid the challenges associated with growth of fastidious organisms; (iii) avoid the biased growth that occurs when only routine culture medium is used; (iv) detect viral, fungal, and parasitic organisms in the same assay; and (v) detect the presence of drug resistance genes. Advances in mNGS technology and data analysis have reduced testing costs to the point where the potential advantages of mNGS over culture and other methods warrant its development for use in clinical settings. In this review, the mNGS approach is discussed, along with a comparison to other methods, limitations, and suggestions for further development and overcoming hurdles to adoption in the clinical setting.
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