Viral metagenome analysis to guide human pathogen monitoring in environmental samples

Abstract

Aims: The aim of this study was to develop and demonstrate an approach for describing the diversity of human pathogenic viruses in an environmentally isolated viral metagenome.

Methods and results: In silico bioinformatic experiments were used to select an optimum annotation strategy for discovering human viruses in virome data sets and applied to annotate a class B biosolid virome. Results from the in silico study indicated that <1% errors in virus identification could be achieved when nucleotide-based search programs (BLASTn or tBLASTx), viral genome only databases and sequence reads >200 nt were considered. Within the 51,925 annotated sequences, 94 DNA and 19 RNA sequences were identified as human viruses. Virus diversity included environmentally transmitted agents such as parechovirus, coronavirus, adenovirus and aichi virus, as well as viruses associated with chronic human infections such as human herpes and hepatitis C viruses.

Conclusions: This study provided a bioinformatic approach for identifying pathogens in a virome data set and demonstrated the human virus diversity in a relevant environmental sample.

Significance and impact of the study: As the costs of next-generation sequencing decrease, the pathogen diversity described by virus metagenomes will provide an unbiased guide for subsequent cell culture and quantitative pathogen analyses and ensures that highly enriched and relevant pathogens are not neglected in exposure and risk assessments.

Figure 1

Box plot of total classification errors for ten human viruses according to read length and annotation method. Total errors include both ambiguous and missing identifications. Groupings are by the read length (100, 200, 400 nt), the BLAST search program (BLASTn, BLASTx, tBLASTx), and the database where ‘nt’ represents nucleotide database, ‘nr’ represents amino acid database, and ‘v’ represents virus only database. In each box the centreline represents the median, the top and bottom of the box represent the 25th and 75th error percentiles, and the lines represent the data spread. Outliers, marked by circles, were outside three standard deviations of the median. Outliers for Rotavirus were >80% error for the BLASTx nr and tBLASTx nt cases and were excluded from the graph. Complete results for each individual virus are listed in Table S2.

Figure 2

Relative abundance of pathogenic viruses in biosolid virome normalized by genome size and the abundance of adenovirus. Inset: Pie chart of sequence identifications (n = 51 000). Human viral pathogens represent <0·1% of total sequences.