Previous infection with seasonal coronaviruses does not protect male Syrian hamsters from challenge with SARS-CoV-2
Bibliographic record
Abstract
SARS-CoV-2 variants and seasonal coronaviruses continue to cause disease and coronaviruses in the animal reservoir pose a constant spillover threat. Importantly, understanding of how previous infection may influence future exposures, especially in the context of seasonal coronaviruses and SARS-CoV-2 variants, is still limited. Here we adopted a step-wise experimental approach to examine the primary immune response and subsequent immune recall toward antigenically distinct coronaviruses using male Syrian hamsters. Hamsters were initially inoculated with seasonal coronaviruses (HCoV-NL63, HCoV-229E, or HCoV-OC43), or SARS-CoV-2 pango B lineage virus, then challenged with SARS-CoV-2 pango B lineage virus, or SARS-CoV-2 variants Beta or Omicron. Although infection with seasonal coronaviruses offered little protection against SARS-CoV-2 challenge, HCoV-NL63-infected animals had an increase of the previously elicited HCoV-NL63-specific neutralizing antibodies during challenge with SARS-CoV-2. On the other hand, primary infection with HCoV-OC43 induced distinct T cell gene signatures. Gene expression profiling indicated interferon responses and germinal center reactions to be induced during more similar primary infection-challenge combinations while signatures of increased inflammation as well as suppression of the antiviral response were observed following antigenically distant viral challenges. This work characterizes and analyzes seasonal coronaviruses effect on SARS-CoV-2 secondary infection and the findings are important for pan-coronavirus vaccine design.
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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) | 0.001 | 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".