SARS-CoV-2 Accessory Protein ORF8 Targets the Dimeric IgA Receptor pIgR
Bibliographic record
Abstract
SARS-CoV-2 is a highly pathogenic respiratory virus that successfully initiates and establishes its infection at the respiratory mucosa. However, little is known about how SARS-CoV-2 antagonizes the host's mucosal immunity. Recent findings have shown a marked reduction in the expression of the polymeric Ig receptor (pIgR) in COVID-19 patients. This receptor maintains mucosal homeostasis by transporting the dimeric IgA (dIgA) and pentameric IgM (pIgM) across mucosal epithelial cells to neutralize the invading respiratory pathogens. By studying the interaction between pIgR and SARS-CoV-2 proteins, we discovered that the viral accessory protein Open Reading Frame 8 (ORF8) potently downregulates pIgR expression and that this downregulation activity of ORF8 correlates with its ability to interact with pIgR. Importantly, the ORF8-mediated downregulation of pIgR diminishes the binding of dIgA or pIgM, and the ORF8 proteins of the variants of concern of SARS-CoV-2 preserve the function of downregulating pIgR, indicating the importance of this conserved activity of ORF8 in SARS-CoV-2 pathogenesis. We further observed that the secreted ORF8 binds to cell surface pIgR, but that this interaction does not trigger the cellular internalization of ORF8, which requires the binding of dIgA to pIgR. These findings suggest the role of ORF8 in SARS-CoV-2 mucosal immune evasion.
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 | 0.000 |
| 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 | 0.001 |
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".