Lack of antibody-mediated cross-protection between SARS-CoV-2 and SARS-CoV infections
Bibliographic record
Abstract
BACKGROUND: The novel coronavirus (SARS-CoV-2) shares approximately 80% whole genome sequence identity and 66% spike (S) protein identity with that of SARS-CoV. The cross-neutralization between these viruses is currently not well-defined. METHODS: Here, by using the live SARS-CoV-2 virus infection assay as well as HIV-1 based pseudotyped-virus carrying the spike (S) gene of the SARS-CoV-2 (ppSARS-2) and SARS-CoV (ppSARS), we examined whether infections with SARS-CoV and SARS-CoV-2 can induce cross-neutralizing antibodies. FINDINGS: We confirmed that SARS-CoV-2 infects cells via angiotensin converting enzyme 2 (ACE2), the functional receptor for SARS-CoV, and we also found that the recombinant receptor binding domain (RBD) of the S protein of SARS-CoV effectively inhibits ppSARS-2 entry in Huh7.5 cells. However, convalescent sera from SARS-CoV and SARS-CoV-2 patients showed high neutralizing activity only against the homologous virus, with no or limited cross-neutralization activity against the other pseudotyped virus. Similar results were also observed in vaccination studies in mice. INTERPRETATION: Our study demonstrates that although both SARS-CoV and SARS-CoV-2 use ACE2 as a cellular receptor, the neutralization epitopes are not shared by these two closely-related viruses, highlighting challenges towards developing a universal vaccine against SARS-CoV related viruses. FUNDING: This work was supported by the National Key Research and Development Program of China, the National Major Project for Control and Prevention of Infectious Disease in China, and the One Belt and One Road Major Project for infectious diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".