Novel sACE2-Anti-CD16VHH Fusion Protein Surreptitiously Inhibits SARS-CoV-2 Variant Spike Proteins and Macrophage Cytokines, and Activates Natural Killer Cell Cytotoxicity
Bibliographic record
Abstract
Background/Objectives: The SARS-CoV-2’s high mutations and replication rates contribute to its high infectivity and resistance to current vaccinations and treatments. The primary cause of resistance to most current treatments aligns within the coding regions for the spike S protein of SARS-CoV-2 that has mutated. As a potential novel immunotherapy, we generated a novel fusion protein composed of a soluble ACE2 (sACE2) linked to llama-derived anti-CD16 that targets different variants of spike proteins and enhances natural killer cells to target infected cells. Methods: Here, we generated a novel sACE2-AntiCD16VHH fusion protein using a Gly4Ser linker, synthesized and cloned into the pLVX-EF1alpha-IRES-Puro vector, and further expressed in ExpiCHO-S cells and purified using Ni+NTA chromatography. Results: The fusion protein significantly blocked SARS-CoV-2 alpha, beta, delta, gamma, and omicron S-proteins binding and activating angiotensin-converting enzyme receptor-2 (ACE2) on ACE2-expressing RAW-Blue macrophage cells and the secretion of several key inflammatory cytokines, G-CSF, MIP-1A, and MCP-1, implicated in the cytokine release storm (CRS). The sACE2-Anti-CD16VHH fusion protein also bridged NK cells to ACE2-expressing human lung carcinoma A549 cells and significantly activated NK-dependent cytotoxicity. Conclusions: The findings show that a VHH directed against CD16 could be an excellent candidate to be linked to soluble ACE2 to generate a bi-specific molecule (sACE2-AntiCD16VHH) suitable for bridging effector cells and infected target cells to inhibit SARS-CoV-2 variant spike proteins binding to the ACE2 receptor in the RAW-Blue cell line and pro-inflammatory cytokines and to activate natural killer cell cytotoxicity.
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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) | 0.001 | 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 | 0.001 |
| 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".