Evidence map›Paper›PMID 34578449›Full record

ArticleViruses2021

Construction of a Recombinant Porcine Epidemic Diarrhea Virus Encoding Nanoluciferase for High-Throughput Screening of Natural Antiviral Products.

Wan Li, Mengjia Zhang, Huijun Zheng, Peng Zhou, Zheng Liu, Anan Jongkaewwattana, Rui Luo, Qigai He

Open access · goldAbstract read
In one paragraph

Article in Viruses, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.4field-weighted citation impact, top 20% 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

11 citing papers in PubMed, 15 citations in OpenAlex.

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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

8 authors at 3 institutions in 2 countries.

Wan LiState Key Laboratory of Agricultural Microbiology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China.
Mengjia ZhangState Key Laboratory of Agricultural Microbiology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China.
Huijun ZhengState Key Laboratory of Agricultural Microbiology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China.
Peng ZhouState Key Laboratory of Agricultural Microbiology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China.
Zheng LiuKey Laboratory of Pesticide & Chemical Biology of Ministry of Education, Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan 430079, China.
Anan JongkaewwattanaVirology and Cell Technology Laboratory, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Thailand Science Park, Phahonyothin Rd., Pathumthani 12120, Thailand.ORCID 0000-0003-2354-9883
Rui LuoState Key Laboratory of Agricultural Microbiology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China.
Qigai HeState Key Laboratory of Agricultural Microbiology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China.
Huazhong Agricultural University · CNCentral China Normal University · CNNational Science and Technology Development Agency · TH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Porcine epidemic diarrhea virus (PEDV) is the predominant cause of an acute, highly contagious enteric disease in neonatal piglets. There are currently no approved drugs against PEDV infection. Here, we report the development of a nanoluciferase (NLuc)-based high-throughput screening (HTS) platform to identify novel anti-PEDV compounds. We constructed a full-length cDNA clone for a cell-adapted PEDV strain YN150. Using reverse genetics, we replaced the open reading frame 3 (ORF3) in the viral genome with an NLuc gene to engineer a recombinant PEDV expressing NLuc (rPEDV-NLuc). rPEDV-NLuc produced similar plaque morphology and showed similar growth kinetics compared with the wild-type PEDV in vitro. Remarkably, the level of luciferase activity could be stably detected in rPEDV-NLuc-infected cells and exhibited a strong positive correlation with the viral titers. Given that NLuc expression represents a direct readout of PEDV replication, anti-PEDV compounds could be easily identified by quantifying the NLuc activity. Using this platform, we screened for the anti-PEDV compounds from a library of 803 natural products and identified 25 compounds that could significantly inhibit PEDV replication. Interestingly, 7 of the 25 identified compounds were natural antioxidants, including Betulonic acid, Ursonic acid, esculetin, lithocholic acid, nordihydroguaiaretic acid, caffeic acid phenethyl ester, and grape seed extract. As expected, all of the antioxidants could potently reduce PEDV-induced oxygen species production, which, in turn, inhibit PEDV replication in a dose-dependent manner. Collectively, our findings provide a powerful platform for the rapid screening of promising therapeutic compounds against PEDV infection.

Indexed as

High-Throughput Screening AssaysMicrobial Sensitivity TestsAnimalsAntioxidantsAntiviral AgentsBiological ProductsChlorocebus aethiopsGenetic EngineeringGenome, ViralLuciferasesPorcine epidemic diarrhea virusReactive Oxygen SpeciesReverse GeneticsVero CellsAntioxidantsAntiviral AgentsBiological ProductsLuciferasesnanolucReactive Oxygen Speciesantiviral compoundshigh-throughput screeningnanoluciferasePEDVreverse genetics system

Identifiers

PMID34578449
PMCPMC8473292
OpenAlexW3200728090

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.