In-Silico Pangenomics of SARS-CoV-2 Isolates Reveal Evidence for Subtle Adaptive Expression Strategies, Continued Clonal Evolution, and Sub-Clonal Emergences, Despite Genome Stability
Bibliographic record
Abstract
The devastating SARS-CoV2 pandemic is worsening with relapsing surges, emerging mutants, and increasing mortalities. Despite enormous efforts, it is not clear how SARS-CoV2 adapts and evolves in a clonal background. Laboratory research is hindered by high biosafety demands. However, the rapid sequence availability opened doors for bioinformatics. Using different bioinformatics programs, we investigated 6305 sequences for clonality, expressions strategies, and evolutionary dynamics. Results showed high nucleotide identity of 99.9% among SARS-CoV2 indicating clonal evolution and genome. High sequence identity and phylogenetic tree concordance were obtained with isolates from different regions. In any given tree topology, ~50% of isolates in a country formed country-specific sub-clusters. However, abundances of subtle overexpression strategies were found including transversions, signature-sequences and slippery-structures. Five different short tracks dominated with identical location patterns in all genomes where Slippery-4 AAGAA was the most abundant. Interestingly, transversion and transition substitutions mostly affected the same amino acid residues implying compensatory changes. To ensure these strategies were independent of sequence clonality, we simultaneously examined sequence homology indicators; tandem-repeats, restriction-site, and 3′UTR, 5′ UTR-caps and stem-loop locations in addition to stringent alignment parameters for 100% identity which all confirmed stability. Nevertheless, two rare events; a rearrangement in two SARS-CoV2 isolates against betacoronavirus ancestor and a polymorphism in S gene, were detected. Thus, we report on abundance of transversions, slippery sequences, and ON/OFF molecular structures, implying adaptive expressions had occurred, despite clonal evolution and genome stability. Furthermore, functional validation of the point mutations would provide insights into mechanisms of SARS-CoV2 virulence and adaptation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".