Inhibition of Respiratory Syncytial Virus Infection by Small Non-Coding RNA Fragments
Bibliographic record
Abstract
Respiratory syncytial virus (RSV) causes acute lower respiratory tract infection in infants, immunocompromised individuals and the elderly. As the only current specific treatment options for RSV are monoclonal antibodies, there is a need for efficacious antiviral treatments against RSV to be developed. We have previously shown that a group of synthetic non-coding single-stranded DNA oligonucleotides with lengths of 25-40 nucleotides can inhibit RSV infection in vitro and in vivo. Based on this, herein, we investigate whether naturally occurring single-stranded small non-coding RNA (sncRNA) fragments present in the airways have antiviral effects against RSV infection. From publicly available sequencing data, we selected sncRNA fragments such as YRNAs, tRNAs and rRNAs present in human bronchoalveolar lavage fluid (BALF) from healthy individuals. We utilized a GFP-expressing RSV to show that pre-treatment with the selected sncRNA fragments inhibited RSV infection in A549 cells in vitro. Furthermore, by using a flow cytometry-based binding assay, we demonstrate that these naturally occurring sncRNAs fragments inhibit viral infection most likely by binding to the RSV entry receptor nucleolin and thereby preventing the virus from binding to host cells, either directly or via steric hindrance. This finding highlights a new function of sncRNAs and displays the possibility of using naturally occurring sncRNAs as treatments against RSV.
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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".