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Molecular Evolutionary Analyses of the Fusion Genes in Human Parainfluenza Virus Type 4.

作者信息

Mizukoshi Fuminori, Kimura Hirokazu, Sugimoto Satoko, Kimura Ryusuke, Nagasawa Norika, Hayashi Yuriko, Hashimoto Koichi, Hosoya Mitsuaki, Shirato Kazuya, Ryo Akihide

机构信息

Department of Virology III, National Institute of Infectious Diseases, Musashimurayama-shi 208-0011, Tokyo, Japan.

Department of Health Science, Graduate School of Health Sciences, Gunma Paz University, Takasaki-shi 370-0006, Gunma, Japan.

出版信息

Microorganisms. 2024 Aug 9;12(8):1633. doi: 10.3390/microorganisms12081633.


DOI:10.3390/microorganisms12081633
PMID:39203475
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11356533/
Abstract

The human parainfluenza virus type 4 (HPIV4) can be classified into two distinct subtypes, 4a and 4b. The full lengths of the gene ( gene) of 48 HPIV4 strains collected during the period of 1966-2022 were analyzed. Based on these gene sequences, the time-scaled evolutionary tree was constructed using Bayesian Markov chain Monte Carlo methods. A phylogenetic tree showed that the first division of the two subtypes occurred around 1823, and the most recent common ancestors of each type, 4a and 4b, existed until about 1940 and 1939, respectively. Although the mean genetic distances of all strains were relatively wide, the distances in each subtype were not wide, indicating that this gene was conserved in each subtype. The evolutionary rates of the genes were relatively low (4.41 × 10 substitutions/site/year). Moreover, conformational B-cell epitopes were predicted in the apex of the trimer fusion protein. These results suggest that HPIV4 subtypes diverged 200 years ago and the progenies further diverged and evolved.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc5d/11356533/0f43a065854b/microorganisms-12-01633-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc5d/11356533/5322b4098938/microorganisms-12-01633-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc5d/11356533/bc517567adb0/microorganisms-12-01633-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc5d/11356533/a868031ce2f1/microorganisms-12-01633-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc5d/11356533/0f43a065854b/microorganisms-12-01633-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc5d/11356533/5322b4098938/microorganisms-12-01633-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc5d/11356533/bc517567adb0/microorganisms-12-01633-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc5d/11356533/a868031ce2f1/microorganisms-12-01633-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc5d/11356533/0f43a065854b/microorganisms-12-01633-g004.jpg

相似文献

[1]
Molecular Evolutionary Analyses of the Fusion Genes in Human Parainfluenza Virus Type 4.

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[2]
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[3]
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[4]
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[5]
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[6]
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[7]
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[8]
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[9]
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[10]
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引用本文的文献

[1]
Shifting respiratory pathogens: Post-COVID-19 trends in community-acquired infections in underserved communities.

PLoS One. 2025-8-22

[2]
Molecular Evolution of the () Genes in Human Parainfluenza Virus Type 2.

Microorganisms. 2025-2-12

[3]
Phylogenomic Analyses of the Hemagglutinin-Neuraminidase () Gene in Human Parainfluenza Virus Type 4 Isolates in Japan.

Microorganisms. 2025-2-10

本文引用的文献

[1]
Functional and structural basis of human parainfluenza virus type 3 neutralization with human monoclonal antibodies.

Nat Microbiol. 2024-8

[2]
Immunoinformatics design of a structural proteins driven multi-epitope candidate vaccine against different SARS-CoV-2 variants based on fynomer.

Sci Rep. 2024-5-4

[3]
DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations.

Front Immunol. 2024

[4]
UCSF ChimeraX: Tools for structure building and analysis.

Protein Sci. 2023-11

[5]
Pathogen Profiles in Outpatients with Non-COVID-19 during the 7th Prevalent Period of COVID-19 in Gunma, Japan.

Microorganisms. 2023-8-24

[6]
Molecular evolutionary analyses of the fusion protein gene in human respirovirus 1.

Virus Res. 2023-8

[7]
Molecular Evolution of Human Parainfluenza Virus Type 2 Based on Hemagglutinin-Neuraminidase Gene.

Microbiol Spectr. 2023-6-15

[8]
SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning.

Front Immunol. 2022

[9]
Respiratory syncytial virus prevention within reach: the vaccine and monoclonal antibody landscape.

Lancet Infect Dis. 2023-1

[10]
ColabFold: making protein folding accessible to all.

Nat Methods. 2022-6

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