False-negative severe acute respiratory syndrome coronavirus 2 test results can negatively impact the clinical and public health response to coronavirus disease 2019 (COVID-19). We used droplet digital polymerase chain reaction (ddPCR) to demonstrate that human DNA levels, a stable molecular marker of sampling quality, were significantly lower in samples from 40 confirmed or suspected COVID-19 cases that yielded negative diagnostic test results (ie, suspected false-negative test results) compared with a representative pool of 87 specimens submitted for COVID-19 testing. Our results support suboptimal biological sampling as a contributor to false-negative COVID-19 test results and underscore the importance of proper training and technique in the collection of nasopharyngeal specimens.

Studying in vivo fitness costs of mutations in viruses provides important insights into their evolutionary dynamics, which can help decipher how they adapt to host immune systems and develop drug resistance. However, studying fitness costs in natural populations is difficult, and is often conducted in vitro where evolutionary dynamics differ from in vivo. We aimed to understand in vivo fitness costs of mutations in Hepatitis C virus using next generation sequencing data. Hepatitis C virus is a positive-sense single-stranded RNA virus, and like many RNA viruses, has extremely high mutation and replication rates, making it ideal for studying mutational fitness costs. Using the ‘frequency-based approach’, we estimated genome-wide in vivo mutation frequencies at mutation-selection equilibrium, and inferred fitness costs (selection coefficients) at every genomic position using data from 195 patients. We applied a beta regression model to estimate the effects and the magnitudes of different factors on fitness costs. We generated a high-resolution genome-wide map of fitness costs in Hepatitis C virus for the first time. Our results revealed that costs of nonsynonymous mutations are three times higher than those of synonymous mutations, and mutations at nucleotides A/T have higher costs than those at C/G. Genome location had a modest effect, which is a clear contrast from previously reported in vitro findings, and highlights host immune selection. We inferred the strongest negative selection on the Core and NS5B proteins. We also found widespread natural prevalence of known drug resistance-associated variants in treatment naive patients, despite high fitness costs of these resistance sites. Our results indicate that in vivo evolutionary patterns and associated mutational costs are dynamic and can be virus specific, reinforcing the utility of constructing in vivo fitness cost maps of viral genomes.Author SummaryUnderstanding how viruses evolve within patients is important for combatting viral diseases, yet studying viruses within patients is difficult. Laboratory experiments are often used to understand the evolution of viruses, in place of assessing the evolution in natural populations (patients), but the dynamics will be different. In this study, we aimed to understand the within-patient evolution of Hepatitis C virus, which is an RNA virus that replicates and mutates extremely quickly, by taking advantage of high-throughput next generation sequencing. Here, we describe the evolutionary patterns of Hepatitis C virus from 195 patients: We analyzed mutation frequencies and estimated how costly each mutation was. We also assessed what factors made a mutation more costly, including the costs associated with drug resistance mutations. We were able to create a genome-wide fitness map of within-patient mutations in Hepatitis C virus which proves that, with technological advances, we can deepen our understanding of within-patient viral evolution, which can contribute to develop better treatments and vaccines.

Improper nasopharyngeal swab collection could contribute to false-negative COVID-19 results. In support of this, specimens from confirmed or suspected COVID-19 cases that tested negative or indeterminate (i.e. suspected false-negatives) contained less human DNA (a stable molecular marker of sampling quality) compared to a representative pool of specimens submitted for testing.

Understanding within-host evolution is critical for predicting viral evolutionary outcomes, yet such studies are currently lacking due to difficulty involving human subjects. Hepatitis C virus (HCV) is an RNA virus with high mutation rates. Its complex evolutionary dynamics and extensive genetic diversity are demonstrated in over 67 known subtypes. In this study, we analyzed within-host mutation frequency patterns of three HCV subtypes, using a large number of samples obtained from treatment-naïve participants by next-generation sequencing. We report that overall mutation frequency patterns are similar among subtypes, yet subtype 3a consistently had lower mutation frequencies and nucleotide diversity, while subtype 1a had the highest. We found that about 50% of genomic sites are highly conserved across subtypes, which are likely under strong purifying selection. We also compared within-host and between-host selective pressures, which revealed that Hyper Variable Region 1 within hosts was under positive selection, but was under slightly negative selection between hosts, which indicates that many mutations created within hosts are removed during the transmission bottleneck. Examining the natural prevalence of known resistance-associated variants showed their consistent existence in the treatment-naïve participants. These results provide insights into the differences and similarities among HCV subtypes that may be used to develop and improve HCV therapies.

Like many viruses, Hepatitis C Virus (HCV) has a high nutation rate, which helps the virus adapt quickly, but mutations come with fitness costs. Fitness costs can be studied by different approaches, such as experimental or frequency-based approaches. The frequency-based approach is particularly useful to estimate in vivo fitness costs, but this approach works best with deep sequencing data from many hosts are. In this study, we applied the frequency-based approach to a large dataset of 195 patients and estimated the fitness costs of mutations at 7957 sites along the HCV genome. We used beta regression and random forest models to better understand how different factors influenced fitness costs. Our results revealed that costs of nonsynonymous mutations were three times higher than those of synonymous mutations, and mutations at nucleotides A or T had higher costs than those at C or G. Genome location had a modest effect, with lower costs for mutations in HVR1 and higher costs for mutations in Core and NS5B. Resistance mutations were, on average, costlier than other mutations. Our results show that in vivo fitness costs of mutations can be site and virus specific, reinforcing the utility of constructing in vivo fitness cost maps of viral genomes.