Evidence map›Paper›PMID 34580386›Full record

ArticleScientific reports2021

AGO CLIP-based imputation of potent siRNA sequences targeting SARS-CoV-2 with antifibrotic miRNA-like activity.

Seung Hyun Ahn, Dowoon Gu, Yongjun Koh, Hye-Sook Lee, Sung Wook Chi

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Seung Hyun Ahn *Department of Life Sciences, Korea University, Seoul, Korea.
Dowoon Gu *Department of Life Sciences, Korea University, Seoul, Korea.
Yongjun KohDivision of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul, Korea.
Hye-Sook LeeDepartment of Life Sciences, Korea University, Seoul, Korea.
Sung Wook ChiDepartment of Life Sciences, Korea University, Seoul, Korea. chi13@korea.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is associated with fatal pulmonary fibrosis. Small interfering RNAs (siRNAs) can be developed to induce RNA interference against SARS-CoV-2, and their susceptible target sites can be inferred by Argonaute crosslinking immunoprecipitation sequencing (AGO CLIP). Here, by reanalysing AGO CLIP data in RNA viruses, we delineated putative AGO binding in the conserved non-structural protein 12 (nsp12) region encoding RNA-dependent RNA polymerase (RdRP) in SARS-CoV-2. We utilised the inferred AGO binding to optimise the local RNA folding parameter to calculate target accessibility and predict all potent siRNA target sites in the SARS-CoV-2 genome, avoiding sequence variants. siRNAs loaded onto AGO also repressed seed (positions 2-8)-matched transcripts by acting as microRNAs (miRNAs). To utilise this, we further screened 13 potential siRNAs whose seed sequences were matched to known antifibrotic miRNAs and confirmed their miRNA-like activity. A miR-27-mimicking siRNA designed to target the nsp12 region (27/RdRP) was validated to silence a synthesised nsp12 RNA mimic in lung cell lines and function as an antifibrotic miR-27 in regulating target transcriptomes related to TGF-β signalling. siRNA sequences with an antifibrotic miRNA-like activity that could synergistically treat COVID-19 are available online ( http://clip.korea.ac.kr/covid19 ).

Indexed as

A549 CellsArgonaute ProteinsBase SequenceBinding SitesCell LineCoronavirus RNA-Dependent RNA PolymeraseCOVID-19Gene Expression ProfilingHeLa CellsHumansMicroRNAsPulmonary FibrosisRNA InterferenceRNA-SeqRNA, Small InterferingSARS-CoV-2Argonaute ProteinsCoronavirus RNA-Dependent RNA PolymeraseMicroRNAsNSP12 protein, SARS-CoV-2RNA, Small Interfering

Identifiers

PMID34580386
PMCPMC8476540

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.