Total and Subgenomic RNA Viral Load in Patients Infected With SARS-CoV-2 Alpha, Delta, and Omicron Variants.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic and subgenomic RNA levels are frequently used as a correlate of infectiousness. The impact of host factors and SARS-CoV-2 lineage on RNA viral load is unclear. METHODS: Total nucleocapsid (N) and subgenomic N (sgN) RNA levels were measured by quantitative reverse transcription polymerase chain reaction (RT-qPCR) in specimens from 3204 individuals hospitalized with coronavirus disease 2019 (COVID-19) at 21 hospitals. RT-qPCR cycle threshold (Ct) values were used to estimate RNA viral load. The impact of time of sampling, SARS-CoV-2 variant, age, comorbidities, vaccination, and immune status on N and sgN Ct values were evaluated using multiple linear regression. RESULTS: Mean Ct values at presentation for N were 24.14 (SD 4.53) for non-variants of concern, 25.15 (SD 4.33) for Alpha, 25.31 (SD 4.50) for Delta, and 26.26 (SD 4.42) for Omicron. N and sgN RNA levels varied with time since symptom onset and infecting variant but not with age, comorbidity, immune status, or vaccination. When normalized to total N RNA, sgN levels were similar across all variants. CONCLUSIONS: RNA viral loads were similar among hospitalized adults, irrespective of infecting variant and known risk factors for severe COVID-19. Total N and subgenomic RNA N viral loads were highly correlated, suggesting that subgenomic RNA measurements add little information for the purposes of estimating infectivity.

Temporal dynamics of SARS-CoV-2 mutation accumulation within and across infected hosts.

Analysis of SARS-CoV-2 genetic diversity within infected hosts can provide insight into the generation and spread of new viral variants and may enable high resolution inference of transmission chains. However, little is known about temporal aspects of SARS-CoV-2 intrahost diversity and the extent to which shared diversity reflects convergent evolution as opposed to transmission linkage. Here we use high depth of coverage sequencing to identify within-host genetic variants in 325 specimens from hospitalized COVID-19 patients and infected employees at a single medical center. We validated our variant calling by sequencing defined RNA mixtures and identified viral load as a critical factor in variant identification. By leveraging clinical metadata, we found that intrahost diversity is low and does not vary by time from symptom onset. This suggests that variants will only rarely rise to appreciable frequency prior to transmission. Although there was generally little shared variation across the sequenced cohort, we identified intrahost variants shared across individuals who were unlikely to be related by transmission. These variants did not precede a rise in frequency in global consensus genomes, suggesting that intrahost variants may have limited utility for predicting future lineages. These results provide important context for sequence-based inference in SARS-CoV-2 evolution and epidemiology.

Temporal dynamics of SARS-CoV-2 mutation accumulation within and across infected hosts.

Analysis of SARS-CoV-2 genetic diversity within infected hosts can provide insight into the generation and spread of new viral variants and may enable high resolution inference of transmission chains. However, little is known about temporal aspects of SARS-CoV-2 intrahost diversity and the extent to which shared diversity reflects convergent evolution as opposed to transmission linkage. Here we use high depth of coverage sequencing to identify within-host genetic variants in 325 specimens from hospitalized COVID-19 patients and infected employees at a single medical center. We validated our variant calling by sequencing defined RNA mixtures and identified a viral load threshold that minimizes false positives. By leveraging clinical metadata, we found that intrahost diversity is low and does not vary by time from symptom onset. This suggests that variants will only rarely rise to appreciable frequency prior to transmission. Although there was generally little shared variation across the sequenced cohort, we identified intrahost variants shared across individuals who were unlikely to be related by transmission. These variants did not precede a rise in frequency in global consensus genomes, suggesting that intrahost variants may have limited utility for predicting future lineages. These results provide important context for sequence-based inference in SARS-CoV-2 evolution and epidemiology.

SARS-CoV-2 Genomic Surveillance Reveals Little Spread From a Large University Campus to the Surrounding Community.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has had high incidence rates at institutions of higher education (IHE) in the United States, but the transmission dynamics in these settings are poorly understood. It remains unclear to what extent IHE-associated outbreaks have contributed to transmission in nearby communities. METHODS: We implemented high-density prospective genomic surveillance to investigate these dynamics at the University of Michigan and the surrounding community during the Fall 2020 semester (August 16-November 24). We sequenced complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from 1659 individuals, including 468 students, representing 20% of cases in students and 25% of total cases in Washtenaw County over the study interval. RESULTS: Phylogenetic analysis identified >200 introductions into the student population, most of which were not related to other student cases. There were 2 prolonged student transmission clusters, of 115 and 73 individuals, that spanned multiple on-campus residences. Remarkably, <5% of nonstudent genomes were descended from student clusters, and viral descendants of student cases were rare during a subsequent wave of infections in the community. CONCLUSIONS: The largest outbreaks among students at the University of Michigan did not significantly contribute to the rise in community cases in Fall 2020. These results provide valuable insights into SARS-CoV-2 transmission dynamics at the regional level.

Neutrophil calprotectin identifies severe pulmonary disease in COVID-19.

Severe cases of coronavirus disease 2019 (COVID-19) are regularly complicated by respiratory failure. While it has been suggested that elevated levels of blood neutrophils associate with worsening oxygenation in COVID-19, it is unknown whether neutrophils are drivers of the thrombo-inflammatory storm or simple bystanders. To better understand the potential role of neutrophils in COVID-19, we measured levels of the neutrophil activation marker S100A8/A9 (calprotectin) in hospitalized patients and determined its relationship to severity of illness and respiratory status. Patients with COVID-19 (n=172) had markedly elevated levels of calprotectin in their blood. Calprotectin tracked with other acute phase reactants including C-reactive protein, ferritin, lactate dehydrogenase, and absolute neutrophil count, but was superior in identifying patients requiring mechanical ventilation. In longitudinal samples, calprotectin rose as oxygenation worsened. When tested on day 1 or 2 of hospitalization (n=94 patients), calprotectin levels were significantly higher in patients who progressed to severe COVID-19 requiring mechanical ventilation (8039 +/- 7031 ng/ml, n=32) as compared to those who remained free of intubation (3365 +/- 3146, p<0.0001). In summary, serum calprotectin levels track closely with current and future COVID-19 severity, implicating neutrophils as potential perpetuators of inflammation and respiratory compromise in COVID-19.

High Prevalence of Extended-Spectrum Beta-Lactamase CTX-M-Producing Escherichia coli in Small-Scale Poultry Farming in Rural Ecuador.

Small-scale farming may have large impacts on the selection and spread of antimicrobial resistance to humans. We conducted an observational study to evaluate antibiotic-resistant Escherichia coli populations from poultry and humans in rural northwestern Esmeraldas, Ecuador. Our study site is a remote region with historically low resistance levels of third-generation antibiotics such cefotaxime (CTX), a clinically relevant antibiotic, in both poultry and humans. Our study revealed 1) high CTX resistance (66.1%) in farmed broiler chickens, 2) an increase in CTX resistance over time in backyard chicken not fed antibiotics (2.3-17.9%), and 3) identical bla CTX-M sequences from human and chicken bacteria, suggesting a spillover event. These findings provide evidence that small-scale meat production operations have direct impacts on the spread and selection of clinically important antibiotics among underdeveloped settings.