Pathogenesis and transmission of SARS-CoV-2 D614G, Alpha, Gamma, Delta, and Omicron variants in golden hamsters
Bibliographic record
Abstract
Since the emergence of SARS-CoV-2 in humans, novel variants have evolved to become dominant circulating lineages. These include D614G (B.1 lineage), Alpha (B.1.1.7), Gamma (P.1), Delta (B.1.617.2), and Omicron BA.1 (B.1.1.529) and BA.2 (B.1.1.529.2) viruses. Here, we compared the viral replication, pathogenesis, and transmissibility of these variants. Replication kinetics and innate immune response against the viruses were tested in ex vivo human nasal epithelial cells (HNEC) and induced pluripotent stem cell-derived lung organoids (IPSC-LOs), and the golden hamster model was employed to test pathogenicity and potential for transmission by the respiratory route. Delta, BA.1, and BA.2 viruses replicated more efficiently, and outcompeted D614G, Alpha, and Gamma viruses in an HNEC competition assay. BA.1 and BA.2 viruses, however, replicated poorly in IPSC-LOs compared to other variants. Moreover, BA.2 virus infection significantly increased secretion of IFN-λ1, IFN-λ2, IFN-λ3, IL-6, and IL-1RA in HNECs relative to D614G infection, but not in IPSC-LOs. The BA.1 and BA.2 viruses replicated less effectively in hamster lungs compared to the other variants; and while the Gamma virus reached titers comparable to D614G and Delta viruses, it caused greater lung pathology. Lastly, the Gamma and Delta variants transmitted more efficiently by the respiratory route compared to the other viruses, while BA.1 and BA.2 viruses transmitted less efficiently. These findings demonstrate the ongoing utility of experimental risk assessment as SARS-CoV-2 variants continue to evolve.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".