bioRxivGeneticsPreprint
Ultra-deep sequencing reveals intra-host diversity and co-infection-driven evolution of SARS-CoV-2
Carol MoragaiD, Francisco Kirhman, Barbara Bernal, Sofia Poblete, Pastor JullianiD, Adrian Gonzalez, Mauricio LatorreiD, Alex Di GenovaiD
Abstract
As COVID-19 enters an endemic phase, SARS-CoV-2 continues to diversify under ongoing immune pressure, with Omicron sublineages and episodic emergent variants sustaining reinfections worldwide. Intra-host evolution represents the earliest stage of this diversification, yet remains undercharacterized, particularly in regions with limited genomic surveillance. Here, we conducted high-throughput sequencing on 96 nasopharyngeal swab samples from Chilean individuals (2020-2022), achieving an average per-base genome coverage of [~]60,000x across the viral genome. This ultra-deep sequencing coverage enabled the identification of intra-host single-nucleotide variants (iSNVs) and co-infection events with high sensitivity and accuracy. Co-infections, especially with Omicron, significantly increased iSNV frequency and recombination, driving viral diversity. Evolutionary analysis based on the non-synonymous to synonymous ratio (dN/dS) shows that Omicron is under extensive purifying selection (global dN/dS [~] 0.55). However, Omicron co-infection cases exhibited higher dN/dS ratios ([~]0.58), suggesting a lower level of purifying selection and increased genetic diversity. Notably, the Spike gene showed dN/dS ratios indicative of positive selection (dN/dS > 1), which are more pronounced in co-infection cases than in Omicron alone. This suggests that co-infections are providing the substrate for the emergence of new variants with enhanced transmissibility and immune evasion capabilities. Together, these findings demonstrate that ultra-deep sequencing is crucial for mapping the evolutionary forces driving SARS-CoV-2 intra-host adaptation and the emergence of new variants.
- organism: virus
- assay: whole-genome sequencing
- research focus: variant calling
- approach: statistical model
- Citations
- 0
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