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Abstract
Measles outbreaks escalated globally despite worldwide elimination efforts. Molecular epidemiological investigations utilizing partial measles virus (MeV) genomes are challenged by reduction in global genotypes and low evolutionary rates. Greater resolution was reached using MeV complete genomes, however time and costs limit the application to numerous samples. We developed an approach to unbiasedly sequence complete MeV genomes directly from patient urine samples. Samples were enriched for MeV using filtration or nucleases and the minimal number of sequence reads to allocate per sample based on its MeV content was assessed using in-silico reduction of sequencing depth. Application of limited-resource sequencing to treated MeV-positive samples demonstrated that 1–5 million sequences for samples with high/medium MeV quantities and 10–15 million sequences for samples with lower MeV quantities are sufficient to obtain >98% MeV genome coverage and over X50 average depth. This approach enables real-time high-resolution molecular epidemiological investigations of large-scale MeV outbreaks.
Highlights
Measles outbreaks, caused by the measles virus (MeV), have escalated globally with a 300% increase in 2019 compared to the previous year [1], including numerous importation events into non-endemic countries
Molecular characterizations based on partial MeV genomes lack the necessary resolution to describe individual transmission events in cases of reduced genotype diversity [2], low number of linked cases, or where MeV is repeatedly imported from an ongoing large outbreak [3]
The content of MeV, human cells and bacteria were measured via real-time RT-PCR prior to and following each treatment to examine its impact
Summary
Measles outbreaks, caused by the measles virus (MeV), have escalated globally with a 300% increase in 2019 compared to the previous year [1], including numerous importation events into non-endemic countries. Generation sequencing (NGS) of MeV complete genomes, applied directly to clinical samples, has the potential to expeditiously and unbiasedly characterize the large volume of samples necessary to analyze during large-scale outbreaks. To reasonably apply the approach to sequence numerous samples during a large-scale outbreak, we minimized NGS costs per sample by limiting the number of allocated sequence reads based on MeV content in the sample. This approach may promote high-resolution characterization of large-scale measles outbreaks at the time they occur
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