25Nearly 400,000 people worldwide are known to have been infected with SARS-CoV-2 beginning 26 in December 2019. The virus has now spread to over 168 countries including the United States, where 27 the first cluster of cases was observed in the Seattle metropolitan area in Washington. Given the rapid 28 increase in the number of cases in many localities, the availability of accurate, high-throughput SARS-29CoV-2 testing is vital to efforts to manage the current public health crisis. In the course of optimizing 30 SARS-CoV-2 testing performed by the University of Washington Clinical Virology Lab (UW Virology Lab), 31 we evaluated assays using seven different primer/probe sets and one assay kit. We found that the most 32 sensitive assays were those that used the E-gene primer/probe set described by Corman et al. 33 (Eurosurveillance 25(3), 2020, https://doi.

Both qualitative and quantitative virologic measurements were compared between blood and genital compartments for 128 men infected with human immunodeficiency virus type 1 (HIV-1) to address several controversial issues concerning HIV-1 shedding in semen and to obtain further information about the distribution of virus between these two compartments. Evidence for viral compartmentalization was suggested by earlier studies that noted the poor correlation between blood and seminal virus load, phenotype, and genotype. Further support for this viral compartmentalization was based on the following observations between semen and blood: lack of association between culturability of virus in semen and viral RNA level in blood, discordant distribution of viral phenotypes, discordant viral RNA levels, a weak correlation between viral RNA level in semen and CD4 cell count in blood, differences in the biologic variability of viral RNA levels, and differences in the virus load response to antiretroviral therapy.

Metagenomic next-generation sequencing (mNGS) is increasingly used for the unbiased detection of viruses, bacteria, fungi, and eukaryotic parasites in clinical samples. Whole-genome sequencing (WGS) of clinical bacterial isolates has been shown to inform hospital infection prevention practices, but this technology has not been utilized during potential respiratory virus outbreaks. Here, we report on the use of mNGS to inform the real-time infection prevention response to a cluster of hospital-acquired human parainfluenza 3 virus (HPIV3) infections at a children's hospital. Samples from 3 patients with hospital-acquired HPIV3 identified over a 12-day period on a general medical unit and 10 temporally associated samples from patients with community-acquired HPIV3 were analyzed. Our sample-to-sequencer time was <24 h, while our sample-to-answer turnaround time was <60 h with a hands-on time of approximately 6 h. Eight (2 cases and 6 controls) of 13 samples had sufficient sequencing coverage to yield the whole genome for HPIV3, while 10 (2 cases and 8 controls) of 13 samples gave partial genomes and all 13 samples had >1 read for HPIV3. Phylogenetic clustering revealed the presence of identical HPIV3 genomic sequence in the two of the cases with hospital-acquired infection, consistent with the concern for recent transmission within the medical unit. Adequate sequence coverage was not recovered for the third case. This work demonstrates the promise of mNGS for providing rapid information for infection prevention in addition to microbial detection.

With the COVID‐19 pandemic caused by SARS‐CoV‐2 now in its second year, there remains an urgent need for diagnostic testing that can identify infected individuals, particularly those who harbor infectious virus. Various RT–PCR strategies have been proposed to identify specific viral RNA species that may predict the presence of infectious virus, including detection of transcriptional intermediates (e.g., subgenomic RNA [sgRNA]) and replicative intermediates (e.g., negative‐strand RNA species). Using a novel primer/probe set for detection of subgenomic (sg)E transcripts, we successfully identified 100% of specimens containing culturable SARS‐CoV‐2 from a set of 126 clinical samples (total sgE CT values ranging from 12.3 to 37.5). This assay showed superior performance compared to a previously published sgRNA assay and to a negative‐strand RNA assay, both of which failed to detect target RNA in a subset of samples from which we isolated live virus. In addition, total levels of viral RNA (genome, negative‐strand, and sgE) detected with the WHO/Charité primer‐probe set correlated closely with levels of infectious virus. Specifically, infectious virus was not detected in samples with a CT above 31.0. Clinical samples with higher levels of viral RNA also displayed cytopathic effect (CPE) more quickly than those with lower levels of viral RNA. Finally, we found that the infectivity of SARS‐CoV‐2 samples is significantly dependent on the cell type used for viral isolation, as Vero E6 cells expressing TMRPSS2 extended the analytical sensitivity of isolation by more than 3 CT compared to parental Vero E6 cells and resulted in faster isolation. Our work shows that using a total viral RNA Ct cutoff of > 31 or specifically testing for sgRNA can serve as an effective rule‐out test for the presence of culturable virus.

Semen is the body fluid most commonly associated with sexual transmission of human immunodeficiency virus type-1 (HIV-1). Because the male genitourinary tract is distinct immunologically from blood, compartment-dependent factors may determine HIV-1 shedding in semen. To identify these factors, the authors obtained 411 semen and blood specimens from 149 men seen up to three times. Seminal plasma was assayed for HIV-1 RNA and semen was cocultured for HIV-1 and cytomegalovirus (CMV), which may up-regulate HIV-1 replication. The best multivariate model for predicting a positive semen HIV-1 coculture included two local urogenital factors, increased seminal polymorphonuclear cell count (odds ratio (OR) = 12.6 for each log10 increase/mL, 95% confidence interval (CI) 12.2, 134.5) and a positive CMV coculture (OR = 3.0, 95% CI 1.2, 7.7). The best multivariate model for predicting semen HIV-1 RNA included two systemic host factors, CD4+ cell counts <200/microliter (OR = 3.0, 95 percent CI 1.3, 6.9) and nucleoside antiretroviral therapy (monotherapy: OR = 0.5, 95% CI 0.3, 1.0; combination therapy: OR = 0.4, 95% CI 0.2, 0.9), and a positive CMV coculture (OR = 1.7, 95% CI 1.0, 3.0). Thus, both systemic and local genitourinary tract factors influence the risk of semen HIV-1 shedding. These findings suggest that measures of systemic virus burden alone may not predict semen infectivity reliably.