Spatio-temporal dynamics of intra-host variability in SARS-CoV-2 genomes

Abstract

During the course of the COVID-19 pandemic, large-scale genome sequencing of SARS-CoV-2 has been useful in tracking its spread and in identifying variants of concern (VOC). Viral and host factors could contribute to variability within a host that can be captured in next-generation sequencing reads as intra-host single nucleotide variations (iSNVs). Analysing 1347 samples collected till June 2020, we recorded 16 410 iSNV sites throughout the SARS-CoV-2 genome. We found ∼42% of the iSNV sites to be reported as SNVs by 30 September 2020 in consensus sequences submitted to GISAID, which increased to ∼80% by 30th June 2021. Following this, analysis of another set of 1774 samples sequenced in India between November 2020 and May 2021 revealed that majority of the Delta (B.1.617.2) and Kappa (B.1.617.1) lineage-defining variations appeared as iSNVs before getting fixed in the population. Besides, mutations in RdRp as well as RNA-editing by APOBEC and ADAR deaminases seem to contribute to the differential prevalence of iSNVs in hosts. We also observe hyper-variability at functionally critical residues in Spike protein that could alter the antigenicity and may contribute to immune escape. Thus, tracking and functional annotation of iSNVs in ongoing genome surveillance programs could be important for early identification of potential variants of concern and actionable interventions.

Within-host diversity of SARS-CoV-2 lineages and effect of vaccination

“…1C; points above the dashed lines). The high frequency of C®U/G®A and A®G/U®C mutants support the hypothesis of RNA editing in vivo via APOlipoprotein B Editing Complex (APOBEC) and Adenosine Deaminase Acting on RNA (ADAR) enzymes 10, 25 , respectively. Interestingly, we observed a higher mutation frequency of G®U, but a lower mutation frequency of C®A, which suggests a strand bias of the G®U mutation.…”

Within-host genetic diversity of SARS-CoV-2 in the context of large-scale hospital-associated genomic surveillance

“…Despite the exceptional level of quality control involved in the generation of our sequences in the clinical context, we found that unavoidable technical artefacts, in particular batch effects and the clear effect of RNA input quantity on minor variant calling-even when limiting samples to those with high coverage across the genome-hampered the ability to draw definitive conclusions in the absence of technical replicates and required us to limit our analyses to high input samples. Our results demonstrate that caution is required when, for example, analyzing minor variants from sequence data repositories 36,37 . Rates of different types of error may meaningfully differ between batches of samples such as sequencing runs, between laboratory protocols and even due to factors such as whether individual tubes or plates were used in library preparation 38,39 .…”

From Minor Mutant to Dominance, the Evolution of SARS-CoV-2 Variants

“…Lineage defining mutations that appeared first as minor mutations in local communities were also observed in India between November 2020 and May 2021 prior to the emergence of the Kappa (B.1.617.1) and Delta (B1.617.2) variants [39] [40]. A possible reason why the Indian subcontinent was so dramatically affected may have been the high prevalence of the A97V (SNP: C13730T) and P323L (SNP: C14408T) mutations in the RNA-dependent RNA polymerase (NSP12) on the subcontinent [39]. Both substitutions correlate with an approximately 100-fold higher evolution rate in India (6,73 x 10 -2 substitutions/site/year) compared to the global average (6,50 x 10 -4 substitutions/site/year) [41].…”