Intra-host genetic diversity of SARS-CoV-2 Omicron variants in children
Authors: Pengcheng Liu, Jiehao Cai, He Tian, Jingjing Li, Lijuan Lu, Menghua Xu, Xunhua Zhu, Huaqing Zhong, Ran Jia, Xiaomin Fu, Xiangshi Wang, Yanling Ge, Yanfeng Zhu, Mei Zeng, Jin Xu
Journal: Journal of Infection (2024)

Dear editor, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of the COVID-19 pandemic, continues evolving and diverging regionally and globally.1Roemer C. Sheward D.J. Hisner R. et al.SARS-CoV-2 evolution in the Omicron era.Nat Microbiol. 2023; 8: 1952-1959Google Scholar, 2Carabelli A.M. Peacock T.P. Thorne L.G. et al.SARS-CoV-2 variant biology: immune escape, transmission and fitness.Nat Rev Microbiol. 2023; 21: 162-177Google Scholar Intra-host single-nucleotide variants (iSNVs) could be a source for the emergence of potentially harmful variants and provide a mutational pool shaping the rapid global evolution of the virus.3Weigang S. Fuchs J. Zimmer G. et al.Within-host evolution of SARS-CoV-2 in an immunosuppressed COVID-19 patient as a source of immune escape variants.Nat Commun. 2021; 12: 6405Google Scholar, 4Li J. Du P. Yang L. et al.Two-step fitness selection for intra-host variations in SARS-CoV-2.Cell Rep. 2022; 38110205Google Scholar In this journal, Zhang and colleagues reported that iSNVs could be found in the SARS-CoV-2 dominant lineages in the samples obtained during the early stage of COVID-19 epidemic and the iSNVs located in spike protein may help in immune escape forecasting.5Zhang Y. Jiang N. Qi W. et al.Intra-host SARS-CoV-2 single-nucleotide variants emerged during the early stage of COVID-19 pandemic forecast population fixing mutations.J Infect. 2022; 84: 722-746Google Scholar Studying SARS-CoV-2 intra-host diversity under different conditions may reveal factors that control virus evolution. Here, we analyzed the intra-host genetic diversity of SARS-CoV-2 Omicron variants in children and its host and viral factors. A total of 64 pediatric cases with symptomatic COVID-19 were enrolled in China in late December 2022. Nasopharyngeal swabs were obtained from the enrolled cases within 3 days after symptom onset. RNA from the samples was extracted using an automatic magnetic extraction device and accompanying kit (Daan Gene Co., Ltd). The SARS-CoV-2 amplicon libraries were generated with 15 μl viral RNA template, by using the VAHTS RNA Multi-PCR Library Prep Kit according to the manufacturer's protocol. Libraries were then sequenced on a Nova Seq instrument (Illumina, San Diego, CA, USA) with 2 × 150-bp paired-end reads. We determined the consensus sequences with iVar 1.2.1, taking the most common base as the consensus (>50% frequency). Lineages of the SARS-CoV-2 consensus sequences obtained were assigned using Pangolin (version 4.2). An iSNV was defined as the alternative allele frequency (AAF) < 50% co-existed with the reference allele at identical genomic position within the same sample, which we use as a metric of intro-host diversity. iSNV sites were identified with the following criteria: (1) base quality larger than 20, (2) AAF ≥ 5%, (3) at least 10 reads support the minor allele, (4) read depth > 200 at iSNV sites, (5) strand bias ratio of reads with the minor allele and reads with major allele less than ten-fold. The genetic diversity of each sample was estimated using Shannon entropy based on the AAF of each iSNV, assuming that all iSNVs are independent from each other.Sn=−∑inPilog2P(i) P(i) is the AAF at variable site i. The comparison of genetic diversities between different groups was performed using the Wilcoxon rank-sum test. Pearson correlation analysis was used for calculating the relationship between age and genetic diversities. All of the tests were two tailed, and a value of P < 0.05 represented statistical significance. All statistical analyses were conducted in SPSS version 26.0 software (IBM, New York, USA). The mean sequencing depth ranged from 1461 to 42,295 per sample. All the samples had ≥90% of the genomic positions with a read depth of more than 200. The variants all belonged to Omicron BA.5 sub-lineages (37 BA.5.2.48 and 27 BF.7.14). We did not observe a significant correlation between the number of iSNVs in a sample and the Ct values of gene ORF1ab and N (Fig. S1ab). Further, we also did not find any correlation between the mean sample coverage depth and the number of iSNVs per sample (Fig. S1c). Thus, the number of iSNVs was unbiased to the viral loads and NGS depths. Intra-host variation was detected in the vast majority of samples (62/64, 96.9%), with a median of 3 iSNVs detected per sample, and 4 (6.2%) samples had more than 10 iSNVs (Fig. 1a). In total, we identified 264 iSNVs, with allele frequency between 6.8% and 49.5% at 113 genomic positions (Fig. 1b). All iSNV sites were examined as a mixture of only two nucleotides, which indicates that multiple nucleotide substitution events have not occurred at a single site during this SARS-COV-2 outbreak. Of these identified iSNV sites, 65.5% (74/113) were uniquely observed in single-patient samples (Fig. 1c). This suggests that most iSNVs are sporadic mutations occurring at distinct positions rather than recurrent mutations occurring at specific hotspots. Next, we studied the distribution of iSNV sites across the viral genome and found that 96.5% (109/113) were located in coding regions; the highest densities of normalized iSNVs/kb were in 5′-UTR, followed by ORF10 and ORF7b (Fig. 1d). We then investigated the iSNVs within each open reading frame (ORF) and detected for non-neutral selection. Considering that if viruses evolve under neutral selection, the iSNVs are likely to distribute equally at each codon position, and the ratio of nonsynonymous substitution to synonymous substitution tends to be similar for all ORFs. In general, most ORFs have iSNVs not equally distributed at codon positions. A lower frequency of SNVs is observed at the third codon position in the S and N genes (Fig. 1e). In addition, the non-synonymous/synonymous ratios of the S and N genes are also higher than the ratio of all of the ORFs (Fig. 1f). These results suggest possible adaptive evolution of SARS-CoV-2 at the intra-host level and the S and N genes that were possibly under more positive selection. Further, we evaluated the host and viral factors influencing the intra-host diversity. No significant difference in the intra-host diversity was observed between BF.7.14 and BA.5.2.48 lineages (Fig. 2a). The intra-host diversity also did not correlate with the age, symptoms of cough or diarrhea, and abnormal chest radiographic (Fig. 2c-f). However, we observed a significantly higher genetic diversity in the females than in males (Fig. 2b). One possible factor underlying female increased diversity is the differences in immune responses during the disease course of SARS-CoV-2 infection in male and female patients.6Takahashi T. Ellingson M.K. Wong P. et al.Sex differences in immune responses that underlie COVID-19 disease outcomes.Nature. 2020; 588: 315-320Google Scholar, 7Wenham C. Smith J. Morgan R. COVID-19: the gendered impacts of the outbreak.Lancet. 2020; 395: 846-848Google Scholar Nonetheless, it is essential for others to use different within-host SARS-CoV-2 sequencing datasets to validate our findings. In conclusion, our study provided insights into the features of SARS-CoV-2 intra-host diversity, selection pressures acting on virus, and factors influencing diversity in children. These results inform our understanding of SARS-CoV-2 diversification in children hosts and highlight important considerations for how they contribute to larger-scale evolutionary patterns. All authors claim no conflict of interest. This study was supported by a Key Development Program of Children's Hospital of Fudan University (No: EK2022ZX05), a grant from Shanghai Municipal Health Commission (No: 202040099), and a 3-Year Action Plan for Strengthening Public Health System in Shanghai (No: GWVI-11.2-YQ43). Download .docx (.09 MB) Help with docx files Supplementary material