Swine acute diarrhea syndrome coronavirus-related viruses from bats show potential interspecies infection
Bibliographic record
Abstract
bats in China and Southeast Asia, but their potential interspecies infection and pathogenicity remain unknown. Herein, we sequenced the spike (S) genes of bat SADSr-CoVs and classified them into four genotypes. We constructed an infectious SADS-CoV cDNA clone (rSADS-CoV) and nine recombinant viruses by replacing the SADS-CoV S gene with that of bat SADSr-CoVs. Recombinant SADSr-CoVs could replicate efficiently in respiratory and intestinal cell lines and human- and swine-derived organoids and caused varying tissue damage and mortality in suckling mice. These viruses can be classified into at least five serotypes based on cross-neutralization assays. Our findings highlight the potential risk of interspecies infection and provide important information for future surveillance of these bat viruses.IMPORTANCEOver the last 20 years, several bat-originated coronaviruses (CoVs), including SARS-CoV, MERS-CoV, and , have caused millions of deaths and severely disrupted global health systems, highlighting the need to investigate bat CoV spillover risks. SADS-CoV, another bat-derived CoV highly pathogenic to piglets, threatens the swine industry and exhibits broad cell tropism, underscoring the need to study these highly diverse viruses with potential for interspecies infection and pathogenicity. As part of our effort to understand these viruses, we developed a framework to characterize them in cell lines and organoids derived from swine and humans, as well as in suckling mice. Additionally, we performed serum cross-neutralization to classify bat SADSr-CoV serotypes, which could guide the development of broad-spectrum vaccines against SADSr-CoVs.
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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".