The origins of mutation in the evolution of SARS-CoV-2 are difficult to assess and especially prevent, as shown Wu et al. Chinese’s team in a paper where incremental mutations and interactions with the host’s immune system were plotted in a one-year retrospective eye.twenty. Quasi-species, well-studied in HIV advances, continue to challenge current SARS-CoV-2 research due to their propensity to see behind mutations, to see deeper into genomic flows, beyond sequence of consensus.14. What is very interesting about what is described as a “cloud of viral mutants” is how these populations are intrinsically selected. The pathogenesis was well described by Sunday et al. in 2019 in other RNA viruses, as an addition of microevolutionary events creating a rich phenotypic intrahost reservoir, moving between domain between variant clouds and interaction within host and intramutant spectratwenty-one. About SARS-CoV-2, studies on quasi-species are rare, but the tendency is to put quasi-species as the main suspect of mutational genesis22.
Here we describe a large study of SARS-CoV-2 quasi-species, in a relatively early population of viruses in the pandemic, notably before the appearance of the delta and Omicron large monophyletic variant of concerns (VOC), and suggest in our persistent population the greatest ability to announce hidden nucleotide events at crucial positions. Persistent COVID-19, as we said before, is a growing entity that suggests high variability within the host and worries the population with immunological damage.19. A recent study, Perez-Lago et al. have shown remarkable intra-host variability of SARS-CoV-2 in three cases of persistent shedding with time course23. They saw mutations arising from genomic weaknesses, especially in the Spike and ORF1ab domains. This finding converges with our results as the most variable positions in our cohort and those that differed from NP were in the Spike and NSP3 domains. The NSP3 gene, encoding papain-like protease (PLpro), has been shown to play an important role in host interactions, through ubiquitin-like action in the inflammatory response and evasion of the immune role of β -interferon type 124. Evidence is also increasing for PLpro’s role in controlling viral spread.25. As the persistence of viral excretion is related to those host-pathogen interactions, we can extrapolate our results saying that higher intra-host variability could be due to those interactions, rather than the other way around.
Furthermore, intra-host variability was discovered especially, in our cohort, in patients with persistent viral shedding. In particular, we detected the same type of subvariant mutations (deletion, transversion, transition) in persistent and non-persistent samples, but at a higher percentage per position in persistent samples. Even if common quasi-species analyzes are studied within a genomic evolutionary timeline composed of several samples in the same patients, we have chosen a different way, shooting quasi-species at time t from one patient sample. Most of the subvariant cloud modifications found in persistent samples were synonymous deletions or mutations, as in several studies on quasi-species26,27,28.29, which could suggest the natural correction and disappearance of these possible sources of mutation. But there is a potential silent role for synonymous mutations, as Khateeb et al. described a significant reduction in infectivity and escape of the BNT162b2 vaccine in a minor part of the nasal pseudovirus population, but with a significant synonymous mutation composition30.
In our analysis of the spike gene (positions 21,563–25,384), ten supervariable positions were found (21,635; 22,063; 22,210; 23,104; 23,144; 23,231; 24,056; 24,290; 24,673 and 25-101), corresponding to amino acids 25, 167, 216, 514, 528, 557, 832, 910, 1037, 1180, respectively. In the literature, Rochelleau et al. has described intra-individual variability in early 2021, mainly in the spike domain, with a positive correlation between high variability by nucleotide location and gene length29. They detected, among 15,289 Sars-CoV-2 genomes analyzed, high-frequency intrahost variability at codon 194, 215, 261, 655, 1254, 1258, and 1259 in the spike domain, which represents a region close to our super codons. variants and appears to be in a similar distribution, close to the key mutations E484, N501 for example. Agius et al. identified high-variable cloud types close to VOC mid-mutations, considering a potential role of that variability strand in deep mutational process, related to strong interactions with our immune system27. In his interesting work, intra-host variability was the most important in the ORF1a domain and in the spike domain as we found for the spike domain and NSP3.
In our cohort, the initial population was different in terms of age and severity, which could have an important impact on the conclusions, instead of finding no link between age and variability in our linear regression analysis. Patients suffering from malignant neoplasms immunosuppressive treatment face a higher risk of COVID-19-related mortality and longer viral shedding. Despite Laubscher et al. showed no further increase in quasi-species in 6 patients from the oncology department 31, our high-throughput analysis showed a greater number of subvariants in persistent detachment, and these discrepancies could be explained with a smaller number of patients than in our study. Furthermore, they did not include samples collected after 3 weeks from diagnosis.
Diabetes mellitus made up a large portion (30%, n = 22) of our persistent compared to nonpersistent patients, and we did not perform any subgroup analysis toward this portion. To the best of our knowledge, studies working on quasi-species in diabetic patients with acute COVID-19 have not yet been reported in the literature, and still need to be built to further understand the evolution of SARS-CoV-2 within the host. We also saw differences between persistent and nonpersistent intrahost genomic variability in mild patients, which confers confidence because persistent viral shedding in mild patients has been associated with longer interaction with the host immune system.32. al-khatib et al. have found a higher intra-host variability in severely ill patients, which differs from our results, probably because there were not enough severely ill patients in the control group, so we cannot conclude with a significant difference26.
In addition, our study suffers from biases, which reside in the fact that the ARTIC protocol is a significant source of variability. In fact, the use of Oxford Nanopore technology is characterized by a higher error rate per base than short-read sequencing techniques. Unless we circumvent this by using a dedicated bioinformatics pipeline to avoid amplification errors (unpublished source), the genome depth we obtained is such that these errors are, in the end, in amounts similar to other NGS techniques. In fact, most viral quasi-species studies use Illumina technology, which is described as more reliable.elevenand we demonstrate here the feasibility of in-depth analysis with Nanopore technology.
An important finding in this work may consist of the presence of N501Y and P681H mutations in the spike domain, in a high percentage in clade 20A samples, sampled before the Alpha (20I) or Omicron (21K) variants increase. Although not all minor variants may emerge as VOCs, intensive sequencing and analysis of SARS-CoV2 quasi-species by NGS, especially in persistent patients, would allow anticipating possible future spread of variants.8. In fact, SARS-CoV-2 cellular entry, which is effective thanks to the spike protein and the ACE2 receptor, can dramatically change by a single different nucleotide, the latter changing the entire 3D conformation of the target to its receptor.33. Furthermore, cell biologists can now not only predict the conformational structure of a nucleotide in the spike domain as a result of mutations, but also the viral affinity for the target cell receptor that results from those modifications.3. 4which remains extremely sensitive as studies revealed particular links between the speed of cell entry of Sars-Cov-2 and clinical severity35. We strongly encourage teams to engage in quasi-species analysis in mass surveillance of variants of concern, as we might stay one step ahead and fill our quiver with another arrow.