Two-step fitness selection for intra-host variations in SARS-CoV-2.

Spontaneous mutations introduce uncertainty into coronavirus disease 2019 (COVID-19) control procedures and vaccine development. Here, we perform a spatiotemporal analysis on intra-host single-nucleotide variants (iSNVs) in 402 clinical samples from 170 affected individuals, which reveals an increase in genetic diversity over time after symptom onset in individuals. Nonsynonymous mutations are overrepresented in the pool of iSNVs but underrepresented at the single-nucleotide polymorphism (SNP) level, suggesting a two-step fitness selection process: a large number of nonsynonymous substitutions are generated in the host (positive selection), and these substitutions tend to be unfixed as SNPs in the population (negative selection). Dynamic iSNV changes in subpopulations with different gender, age, illness severity, and viral shedding time displayed a varied fitness selection process among populations. Our study highlights that iSNVs provide a mutational pool shaping the rapid global evolution of the virus.

COVID-19 reinfection in the presence of neutralizing antibodies.

After a short recovery period, COVID-19 reinfections could occur in convalescent patients, even those with measurable levels of neutralizing antibodies. Effective vaccinations and protective public health measures are recommended for the convalescent COVID-19 patients.

MINERVA: A Facile Strategy for SARS-CoV-2 Whole-Genome Deep Sequencing of Clinical Samples.

Analyzing the genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from clinical samples is crucial for understanding viral spread and evolution as well as for vaccine development. Existing RNA sequencing methods are demanding on user technique and time and, thus, not ideal for time-sensitive clinical samples; these methods are also not optimized for high performance on viral genomes. We developed a facile, practical, and robust approach for metagenomic and deep viral sequencing from clinical samples. We demonstrate the utility of our approach on pharyngeal, sputum, and stool samples collected from coronavirus disease 2019 (COVID-19) patients, successfully obtaining whole metatranscriptomes and complete high-depth, high-coverage SARS-CoV-2 genomes with high yield and robustness. With a shortened hands-on time from sample to virus-enriched sequencing-ready library, this rapid, versatile, and clinic-friendly approach will facilitate molecular epidemiology studies during current and future outbreaks.

Genomic surveillance of COVID-19 cases in Beijing.

The spread of SARS-CoV-2 in Beijing before May, 2020 resulted from transmission following both domestic and global importation of cases. Here we present genomic surveillance data on 102 imported cases, which account for 17.2% of the total cases in Beijing. Our data suggest that all of the cases in Beijing can be broadly classified into one of three groups: Wuhan exposure, local transmission and overseas imports. We classify all sequenced genomes into seven clusters based on representative high-frequency single nucleotide polymorphisms (SNPs). Genomic comparisons reveal higher genomic diversity in the imported group compared to both the Wuhan exposure and local transmission groups, indicating continuous genomic evolution during global transmission. The imported group show region-specific SNPs, while the intra-host single nucleotide variations present as random features, and show no significant differences among groups. Epidemiological data suggest that detection of cases at immigration with mandatory quarantine may be an effective way to prevent recurring outbreaks triggered by imported cases. Notably, we also identify a set of novel indels. Our data imply that SARS-CoV-2 genomes may have high mutational tolerance.