Metagenomic sequencing complements routine diagnostics in identifying viral pathogens in lung transplant recipients with unknown etiology of respiratory infection.

BACKGROUND: Lung transplant patients are a vulnerable group of immunosuppressed patients that are prone to frequent respiratory infections. We studied 60 episodes of respiratory symptoms in 71 lung transplant patients. Almost half of these episodes were of unknown infectious etiology despite extensive routine diagnostic testing. METHODS: We re-analyzed respiratory samples of all episodes with undetermined etiology in order to detect potential viral pathogens missed/not accounted for in routine diagnostics. Respiratory samples were enriched for viruses by filtration and nuclease digestion, whole nucleic acids extracted and randomly amplified before high throughput metagenomic virus sequencing. Viruses were identified by a bioinformatic pipeline and confirmed and quantified using specific real-time PCR. RESULTS: In completion of routine diagnostics, we identified and confirmed a viral etiology of infection by our metagenomic approach in four patients (three Rhinovirus A, one Rhinovirus B infection) despite initial negative results in specific multiplex PCR. Notably, the majority of samples were also positive for Torque teno virus (TTV) and Human Herpesvirus 7 (HHV-7). While TTV viral loads increased with immunosuppression in both throat swabs and blood samples, HHV-7 remained at low levels throughout the observation period and was restricted to the respiratory tract. CONCLUSION: This study highlights the potential of metagenomic sequencing for virus diagnostics in cases with previously unknown etiology of infection and in complex diagnostic situations such as in immunocompromised hosts.

MinVar: A rapid and versatile tool for HIV-1 drug resistance genotyping by deep sequencing.

Genotypic monitoring of drug-resistance mutations (DRMs) in HIV-1 infected individuals is strongly recommended to guide selection of the initial antiretroviral therapy (ART) and changes of drug regimens. Traditionally, mutations conferring drug resistance are detected by population sequencing of the reverse transcribed viral RNA encoding the HIV-1 enzymes target by ART, followed by manual analysis and interpretation of Sanger sequencing traces. This process is labor intensive, relies on subjective interpretation from the operator, and offers limited sensitivity as only mutations above 20% frequency can be reliably detected. Here we present MinVar, a pipeline for the analysis of deep sequencing data, which allows reliable and automated detection of DRMs down to 5%. We evaluated MinVar with data from amplicon sequencing of defined mixtures of molecular virus clones with known DRM and plasma samples of viremic HIV-1 infected individuals and we compared it to VirVarSeq, another virus variant detection tool exclusively working on Illumina deep sequencing data. MinVar was designed to be compatible with a diverse range of sequencing platforms and allows the detection of DRMs and insertions/deletions from deep sequencing data without the need to perform additional bioinformatics analysis, a prerequisite to a widespread implementation of HIV-1 genotyping using deep sequencing in routine diagnostic settings.