Evidence map›Paper›PMID 37192911›Full record

ArticleCell insight2023

Atlas of interactions between SARS-CoV-2 macromolecules and host proteins.

Guangnan Li, Zhidong Tang, Weiliang Fan, Xi Wang, Li Huang, Yu Jia, Manli Wang, Zhihong Hu, Yu Zhou

Open access · goldAbstract read
In one paragraph

Article in Cell insight, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
3.1field-weighted citation impact, top 7% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

15 citing papers in PubMed, 20 citations in OpenAlex.

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  14. SARS-CoV-2 and the DNA damage response.The Journal of general virology · 2023
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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

9 authors at 2 institutions in 1 country.

Guangnan LiCollege of Life Sciences, TaiKang Center for Life and Medical Sciences, RNA Institute, Wuhan University, Wuhan, China.
Zhidong TangCollege of Life Sciences, TaiKang Center for Life and Medical Sciences, RNA Institute, Wuhan University, Wuhan, China.
Weiliang FanCollege of Life Sciences, TaiKang Center for Life and Medical Sciences, RNA Institute, Wuhan University, Wuhan, China.
Xi WangState Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan, China.
Li HuangCollege of Life Sciences, TaiKang Center for Life and Medical Sciences, RNA Institute, Wuhan University, Wuhan, China.
Yu JiaCollege of Life Sciences, TaiKang Center for Life and Medical Sciences, RNA Institute, Wuhan University, Wuhan, China.
Manli WangState Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan, China.
Zhihong HuState Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan, China.
Yu ZhouCollege of Life Sciences, TaiKang Center for Life and Medical Sciences, RNA Institute, Wuhan University, Wuhan, China.
Wuhan University · CNChinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The proteins and RNAs of viruses extensively interact with host proteins after infection. We collected and reanalyzed all available datasets of protein-protein and RNA-protein interactions related to SARS-CoV-2. We investigated the reproducibility of those interactions and made strict filters to identify highly confident interactions. We systematically analyzed the interaction network and identified preferred subcellular localizations of viral proteins, some of which such as ORF8 in ER and ORF7A/B in ER membrane were validated using dual fluorescence imaging. Moreover, we showed that viral proteins frequently interact with host machinery related to protein processing in ER and vesicle-associated processes. Integrating the protein- and RNA-interactomes, we found that SARS-CoV-2 RNA and its N protein closely interacted with stress granules including 40 core factors, of which we specifically validated G3BP1, IGF2BP1, and MOV10 using RIP and Co-IP assays. Combining CRISPR screening results, we further identified 86 antiviral and 62 proviral factors and associated drugs. Using network diffusion, we found additional 44 interacting proteins including two proviral factors previously validated. Furthermore, we showed that this atlas could be applied to identify the complications associated with COVID-19. All data are available in the AIMaP database (https://mvip.whu.edu.cn/aimap/) for users to easily explore the interaction map.

Indexed as

Drug repurposingProtein-interactomeProtein localizationRNA-InteractomeSARS-CoV-2

Identifiers

PMID37192911
PMCPMC9670597
OpenAlexW4309204504

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.