RESUMEN
In the field of diagnostic microbiology, rapid molecular methods are critically important for detecting pathogens. With rapid and accurate detection, preventive measures can be put in place early, thereby preventing loss of life and further spread of a disease. From a preparedness perspective, early detection and response are important in order to minimize the consequences. During the past 2 decades, advances in next-generation sequencing (NGS) technology have changed the playing field of molecular methods. Today, it is within reach to completely sequence the total microbiological content of a clinical sample, creating a metagenome, in a single week of laboratory work. As new technologies emerge, their dissemination and capacity building must be facilitated, and criteria for use, as well as guidelines on how to report results, must be established. This article focuses on the use of metagenomics, from sample collection to data analysis and to some extent NGS, for the detection of pathogens, the integration of the technique in outbreak response systems, and the risk-based evaluation of sample processing in routine diagnostics labs. The article covers recent advances in the field, current debate, gaps in research, and future directions. Examples of metagenomic detection, as well as possible applications of the methods, are described in various biopreparedness outbreak scenarios.
Asunto(s)
Enfermedades de los Animales/epidemiología , Enfermedades de los Animales/microbiología , Bioterrorismo , Brotes de Enfermedades , Metagenómica/métodos , Métodos Analíticos de la Preparación de la Muestra , Animales , Creación de Capacidad , Defensa Civil , Biología Computacional , Humanos , Análisis de Secuencia de ADNRESUMEN
Compared to routine diagnostics, screening for pathogens in outbreak situations, with or without intentional release, poses demands on the detection technology to not only indicate the presence of already known causative agents but also novel and unexpected pathogens. The metagenomic approach to detecting viral pathogens, using unbiased high-throughput sequencing (HTS), is a well-established methodology with a broad detection range and wide applicability on different sample matrices. To prepare a sample for HTS, the common presequencing steps include homogenization, enrichment, separation (eg, magnetic separation), and amplification. In this initial study, we explored the benefits and drawbacks of preprocessing by sequence-independent, single-primer amplification (SISPA) of nucleic acids by applying the methodology to artificial samples. More specifically, a synthetic metagenome was divided into 2 samples, 1 unamplified and 1 diluted, and amplified by SISPA. Subsequently, both samples were sequenced using the Ion Torrent Personal Genome Machine (PGM), and the resulting datasets were analyzed by using bioinformatics, short read mapping, de novo assembly, BLAST-based taxonomic classification, and visualization. The results indicate that even though SISPA introduces a strong amplification bias, which makes it unsuitable for whole-genome sequencing, it is still useful for detecting and identifying viruses.