A minimally-edited mouse model for infection with multiple SARS-CoV-2 strains
Bibliographic record
Abstract
Efficient mouse models to study SARS-CoV-2 infection are critical for the development and assessment of vaccines and therapeutic approaches to mitigate the current pandemic and prevent reemergence of COVID-19. While the first generation of mouse models allowed SARS-CoV-2 infection and pathogenesis, they relied on ectopic expression and non-physiological levels of human angiotensin-converting enzyme 2 (hACE2). Here we generated a mouse model carrying the minimal set of modifications necessary for productive infection with multiple strains of SARS-CoV-2. Substitution of only three amino acids in the otherwise native mouse Ace2 locus (Ace2TripleMutant or Ace2™), was sufficient to render mice susceptible to both SARS-CoV-2 strains USA-WA1/2020 and B.1.1.529 (Omicron). Infected Ace2™ mice exhibited weight loss and lung damage and inflammation, similar to COVID-19 patients. Previous exposure to USA-WA1/2020 or mRNA vaccination generated memory B cells that participated in plasmablast responses during breakthrough B.1.1.529 infection. Thus, the Ace2™ mouse replicates human disease after SARS-CoV-2 infection and provides a tool to study immune responses to sequential infections in mice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".