Mycoplasma synoviae lipid-associated membrane proteins identification and expression changes when exposed to chicken cells
Mycoplasma synoviae is a significant cause of respiratory disease and synovitis among chickens, and has an adverse economic impact on broiler breeding efforts. The present study was designed to develop a systematic understanding of the role that M. synoviae lipid-associated membrane proteins (LAMPs)...
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Frontiers Media S.A.
2023-11-01
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Series: | Frontiers in Veterinary Science |
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Online Access: | https://www.frontiersin.org/articles/10.3389/fvets.2023.1249499/full |
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author | Duoduo Si Jialin Sun Lei Guo Fei Yang Jidong Li Shenghu He |
author_facet | Duoduo Si Jialin Sun Lei Guo Fei Yang Jidong Li Shenghu He |
author_sort | Duoduo Si |
collection | DOAJ |
description | Mycoplasma synoviae is a significant cause of respiratory disease and synovitis among chickens, and has an adverse economic impact on broiler breeding efforts. The present study was designed to develop a systematic understanding of the role that M. synoviae lipid-associated membrane proteins (LAMPs) may play in the virulence of this pathogen. Bioinformatics tools were used to identify 146 predicted membrane proteins and lipoproteins in the M. synoviae proteome. Then, Triton X-114 was used to extract LAMPs that were subsequently identified via LC–MS/MS. This approach enabled the detection of potential LAMPs, and the top 200 most abundant proteins detected using this strategy were subject to further analysis. M. synoviae cells (100 MOI) were exposed to chicken fibroblasts (DF-1) and macrophages (HD-11) in a 1:1 mixed culture. Analysis of LAMP transcripts identified 72 up-regulated LAMP genes which were analyzed in depth by bioinformatics. GO analysis revealed these genes to be enriched in the nucleotide binding, sulfur amino acid transmembrane transporter activity, tRNA binding, rRNA modification, and transition metal ion transport pathways. Moreover, KEGG enrichment analysis suggested that these genes were enriched in the biosynthesis of secondary metabolites, carbon metabolism, glycolysis/gluconeogenesis, and nitrogen metabolism pathways. |
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issn | 2297-1769 |
language | English |
last_indexed | 2024-03-11T13:47:20Z |
publishDate | 2023-11-01 |
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spelling | doaj.art-82a49b5cc48f468cbe2776d994aaca592023-11-02T10:07:12ZengFrontiers Media S.A.Frontiers in Veterinary Science2297-17692023-11-011010.3389/fvets.2023.12494991249499Mycoplasma synoviae lipid-associated membrane proteins identification and expression changes when exposed to chicken cellsDuoduo Si0Jialin Sun1Lei Guo2Fei Yang3Jidong Li4Shenghu He5College of Animal Science and Technology, Clinical Veterinary Laboratory, Ningxia University, Yinchuan, ChinaCollege of Animal Science and Technology, Clinical Veterinary Laboratory, Ningxia University, Yinchuan, ChinaNingxia Xiaoming Agriculture and Animal Husbandry Co., Ltd., Yinchuan, ChinaCollege of Animal Science and Technology, Clinical Veterinary Laboratory, Ningxia University, Yinchuan, ChinaCollege of Animal Science and Technology, Clinical Veterinary Laboratory, Ningxia University, Yinchuan, ChinaCollege of Animal Science and Technology, Clinical Veterinary Laboratory, Ningxia University, Yinchuan, ChinaMycoplasma synoviae is a significant cause of respiratory disease and synovitis among chickens, and has an adverse economic impact on broiler breeding efforts. The present study was designed to develop a systematic understanding of the role that M. synoviae lipid-associated membrane proteins (LAMPs) may play in the virulence of this pathogen. Bioinformatics tools were used to identify 146 predicted membrane proteins and lipoproteins in the M. synoviae proteome. Then, Triton X-114 was used to extract LAMPs that were subsequently identified via LC–MS/MS. This approach enabled the detection of potential LAMPs, and the top 200 most abundant proteins detected using this strategy were subject to further analysis. M. synoviae cells (100 MOI) were exposed to chicken fibroblasts (DF-1) and macrophages (HD-11) in a 1:1 mixed culture. Analysis of LAMP transcripts identified 72 up-regulated LAMP genes which were analyzed in depth by bioinformatics. GO analysis revealed these genes to be enriched in the nucleotide binding, sulfur amino acid transmembrane transporter activity, tRNA binding, rRNA modification, and transition metal ion transport pathways. Moreover, KEGG enrichment analysis suggested that these genes were enriched in the biosynthesis of secondary metabolites, carbon metabolism, glycolysis/gluconeogenesis, and nitrogen metabolism pathways.https://www.frontiersin.org/articles/10.3389/fvets.2023.1249499/fullMycoplasma synoviaeproteomebioinformatics analysislipid-associated membrane proteintranscript analysis |
spellingShingle | Duoduo Si Jialin Sun Lei Guo Fei Yang Jidong Li Shenghu He Mycoplasma synoviae lipid-associated membrane proteins identification and expression changes when exposed to chicken cells Frontiers in Veterinary Science Mycoplasma synoviae proteome bioinformatics analysis lipid-associated membrane protein transcript analysis |
title | Mycoplasma synoviae lipid-associated membrane proteins identification and expression changes when exposed to chicken cells |
title_full | Mycoplasma synoviae lipid-associated membrane proteins identification and expression changes when exposed to chicken cells |
title_fullStr | Mycoplasma synoviae lipid-associated membrane proteins identification and expression changes when exposed to chicken cells |
title_full_unstemmed | Mycoplasma synoviae lipid-associated membrane proteins identification and expression changes when exposed to chicken cells |
title_short | Mycoplasma synoviae lipid-associated membrane proteins identification and expression changes when exposed to chicken cells |
title_sort | mycoplasma synoviae lipid associated membrane proteins identification and expression changes when exposed to chicken cells |
topic | Mycoplasma synoviae proteome bioinformatics analysis lipid-associated membrane protein transcript analysis |
url | https://www.frontiersin.org/articles/10.3389/fvets.2023.1249499/full |
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