Evidence map›Paper›PMID 31592345›Full record

ArticlePeerJ2019

Tandem mass tag-based quantitative proteomic analysis of lycorine treatment in highly pathogenic avian influenza H5N1 virus infection.

Li Yang, Jia Hao Zhang, Xiao Li Zhang, Guang Jie Lao, Guan Ming Su, Lei Wang, Yao Lan Li, Wen Cai Ye, Jun He

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.9field-weighted citation impact, top 15% 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

13 citing papers in PubMed, 28 citations in OpenAlex.

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  13. Research Progress on Anti-Inflammatory Effects and Mechanisms of Alkaloids from Chinese Medical Herbs.Evidence-based complementary and alternative medicine : eCAM · 2020
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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.

Li Yang *Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research, Jinan University, Guangzhou, China.
Jia Hao Zhang *College of Veterinary Medicine, South China Agricultural University, Guangzhou, China.
Xiao Li ZhangCollege of Life Science and Technology, Jinan University, Guangzhou, China.
Guang Jie LaoCollege of Veterinary Medicine, South China Agricultural University, Guangzhou, China.
Guan Ming SuCollege of Veterinary Medicine, South China Agricultural University, Guangzhou, China.
Lei WangGuangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research, Jinan University, Guangzhou, China.
Yao Lan LiGuangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research, Jinan University, Guangzhou, China.
Wen Cai YeGuangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research, Jinan University, Guangzhou, China.
Jun HeGuangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research, Jinan University, Guangzhou, China.
Jinan University · CNSouth China Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Highly pathogenic H5N1 influenza viruses (HPAIV) cause rapid systemic illness and death in susceptible animals, leading to a disease with high morbidity and mortality rates. Although vaccines and drugs are the best solution to prevent this threat, a more effective treatment for H5 strains of influenza has yet to be developed. Therefore, the development of therapeutics/drugs that combat H5N1 influenza virus infection is becoming increasingly important. Lycorine, the major component of Amaryllidaceae alkaloids, exhibits better protective effects against A/CK/GD/178/04 (H5N1) (GD178) viruses than the commercial neuraminidase (NA) inhibitor oseltamivir in our prior study. Lycorine demonstrates outstanding antiviral activity because of its inhibitory activity against the export of viral ribonucleoprotein complexes (vRNPs) from the nucleus. However, how lycorine affects the proteome of AIV infected cells is unknown. Therefore, we performed a comparative proteomic analysis to identify changes in protein expression in AIV-infected Madin-Darby Canine Kidney cells treated with lycorine. Three groups were designed: mock infection group (M), virus infection group (V), and virus infection and lycorine-treated after virus infection group (L). The multiplexed tandem mass tag (TMT) approach was employed to analyze protein level in this study. In total, 5,786 proteins were identified from the three groups of cells by using TMT proteomic analysis. In the V/M group, 1,101 proteins were identified, of which 340 differentially expressed proteins (DEPs) were determined during HPAIV infection; among the 1,059 proteins identified from the lycorine-treated group, 258 proteins presented significant change. Here, 71 proteins showed significant upregulation or downregulation of expression in the virus-infected/mock and virus-infected/lycorine-treated comparisons, and the proteins in each fraction were functionally classified further. Interestingly, lycorine treatment decreased the levels of the nuclear pore complex protein 93 (Nup93, E2RSV7), which is associated with nuclear-cytoplasmic transport. In addition, Western blot experiments confirmed that the expression of Nup93 was significantly downregulated in lycorine treatment but induced after viral infection. Our results may provide new insights into how lycorine may trap vRNPs in the nucleus and suggest new potential therapeutic targets for influenza virus.

Indexed as

Highly pathogenic avian influenza virusLycorineMultiplex tandem mass tagNuclear-cytoplasmic transportNuclear pore complex protein 93

Identifiers

PMID31592345
PMCPMC6778435
OpenAlexW2979256303

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.