Unbiased parallel detection of viral pathogens in clinical samples by use of a metagenomic approach

Abstract

Viral infectious diseases represent a major threat to public health and are among the greatest disease burdens worldwide. Rapid and accurate identification of viral agents is crucial for both outbreak control and estimating regional disease burdens. Recently developed metagenomic methods have proven to be powerful tools for simultaneous pathogen detection. Here, we performed a systematic study of the capability of the short-read-based metagenomic approach in the molecular detection of viral pathogens in nasopharyngeal aspirate samples from patients with acute lower respiratory tract infections (n = 16). Using the high-throughput capacity of ultradeep sequencing and a dedicated data interpretation method, we successfully identified seven species of known respiratory viral agents from 15 samples, a result that was consistent with results of conventional PCR assays. We also detected a coinfected case that was missed by regular PCR testing. Using the metagenomic data, 11 draft genomes of the abundantly detected viruses in the samples were reconstructed with 21.84% to 98.53% coverage. Our results show the power of the short-read-based metagenomic approach for accurate and parallel screening of viral pathogens. Although there are some inherent difficulties in applying this approach to clinical samples, including a lack of controls, limited specimen quantity, and high contamination rate, our work will facilitate further application of this unprecedented high-throughput method to clinical samples.

Fig. 1.

Pie chart of taxonomic distribution of ultradeep sequencing reads from clinical samples. Data are average values from 16 NPA samples.

Fig. 2.

Relationship between VTMK index of metagenomic approach and HRSV copy number per ng of total RNA extracted from the NPA samples determined by quantitative PCR. The standard deviations of three quantitative PCR repeats of each sample are given, and an exponential regression line is fitted to the data.