Prolific Induction of IL-6 in Human Cells by SARS-CoV-2-derived Peptide is Attenuated by Recombinant Human Anti-inflammatory Cytokines made <i>in planta</i>
Bibliographic record
Abstract
SUMMARY Development of efficient therapies for COVID-19 is the focus of intense research. The cytokine release syndrome was underlined as a culprit for severe outcomes in COVID-19 patients. Interleukin-6 (IL-6) plays a crucial role in human immune responses and elevated IL-6 plasma levels have been associated with the exacerbated COVID-19 pathology. Since non-structural protein 10 (NSP10) of SARS-CoV-2 has been implicated in the induction of IL-6, we designed Peptide (P)1, containing sequences corresponding to amino acids 68-96 of NSP10, and examined its effect on cultured human cells. Treatment with P1 strongly increased IL-6 secretion by the lung cancer cell line NCI-H1792 and the breast cancer cell line MDA-MB-231 and revealed profound cytotoxic activity on Caco-2 colorectal adenocarcinoma cells. Treatment with P2, harbouring a mutation in the zinc knuckle motif of NSP10, caused no IL-6 induction and no cytotoxicity. Pre-treatment with plant-produced human anti-inflammatory cytokines IL-37b and IL-38 effectively mitigated the induction of IL-6 secretion. Our results suggest a role for the zinc knuckle motif of NSP10 in the onset of increased IL-6 plasma levels of COVID-19 patients and for IL-37b and IL-38 as therapeutics aimed at attenuating the cytokine release syndrome.
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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 | 0.001 |
| 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".