Generation and Characterization of a SARS-CoV-2-Susceptible Mouse Model Using Adeno-Associated Virus (AAV6.2FF)-Mediated Respiratory Delivery of the Human ACE2 Gene
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the aetiological agent of coronavirus disease 2019 (COVID-19) that has caused a pandemic with millions of human infections. There continues to be a pressing need to develop potential therapies and vaccines to inhibit SARS-CoV-2 infection to mitigate the ongoing pandemic. Epidemiological data from the current pandemic indicates that there may be sex-dependent differences in disease outcomes. To investigate these differences, we proposed to use common small animal species that are frequently used to model disease with viruses. However, common laboratory strains of mice are not readily infected by SARS-CoV-2 because of differences in the angiotensin-converting enzyme 2 (ACE2), the cellular receptor for the virus. To overcome this limitation, we transduced common laboratory accessible strains of mice of different sexes and age groups with a novel a triple AAV6 mutant, termed AAV6.2FF, encoding either human ACE2 or luciferase via intranasal administration to promote expression in the lung and nasal turbinates. Infection of AAV-hACE2-transduced mice with SARS-CoV-2 resulted in high viral titers in the lungs and nasal turbinates, establishment of an IgM and IgG antibody response, and modulation of lung and nasal turbinate cytokine profiles. There were insignificant differences in infection characteristics between age groups and sex-related differences; however, there were significant strain-related differences between BALB/c vs. C57BL/6 mice. We show that AAV-hACE2-transduced mice are a useful for determining immune responses and for potential evaluation of SARS-CoV-2 vaccines and antiviral therapies, and this study serves as a model for the utility of this approach to rapidly develop small-animal models for emerging viruses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".