Virus Identification in Unknown Tropical Febrile Illness Cases Using Deep Sequencing

Abstract

Dengue virus is an emerging infectious agent that infects an estimated 50–100 million people annually worldwide, yet current diagnostic practices cannot detect an etiologic pathogen in ∼40% of dengue-like illnesses. Metagenomic approaches to pathogen detection, such as viral microarrays and deep sequencing, are promising tools to address emerging and non-diagnosable disease challenges. In this study, we used the Virochip microarray and deep sequencing to characterize the spectrum of viruses present in human sera from 123 Nicaraguan patients presenting with dengue-like symptoms but testing negative for dengue virus. We utilized a barcoding strategy to simultaneously deep sequence multiple serum specimens, generating on average over 1 million reads per sample. We then implemented a stepwise bioinformatic filtering pipeline to remove the majority of human and low-quality sequences to improve the speed and accuracy of subsequent unbiased database searches. By deep sequencing, we were able to detect virus sequence in 37% (45/123) of previously negative cases. These included 13 cases with Human Herpesvirus 6 sequences. Other samples contained sequences with similarity to sequences from viruses in the Herpesviridae, Flaviviridae, Circoviridae, Anelloviridae, Asfarviridae, and Parvoviridae families. In some cases, the putative viral sequences were virtually identical to known viruses, and in others they diverged, suggesting that they may derive from novel viruses. These results demonstrate the utility of unbiased metagenomic approaches in the detection of known and divergent viruses in the study of tropical febrile illness. Dengue virus infection is a global health concern, affecting as many as 100 million people annually worldwide. A critical first step to proper treatment and control of any virus infection is a correct diagnosis. Traditional diagnostic tests for viruses depend on amplification of conserved portions of the viral genome, detection of the binding of antibodies to viral proteins, or replication of the virus in cell cultures. These methods have a major shortcoming: they are unable to detect divergent or novel viruses for which a priori sequence, serological, or cellular tropism information is not known. In our study, we use two approaches, microarrays and deep sequencing, to virus identification that are less susceptible to such shortcomings. We used these unbiased tools to search for viruses in blood collected from Nicaraguan children with clinical symptoms indicating dengue virus infection, but for whom current dengue virus detection assays yielded negative results. We were able to identify both known and divergent viruses in about one third of previously negative samples, demonstrating the utility of these approaches to detect viruses in cases of unknown dengue-like illness.