Literature DB >> 28361233

RNA Seq analysis for transcriptome profiling in response to classical swine fever vaccination in indigenous and crossbred pigs.

Shalu Kumari Pathak1, Amit Kumar2, G Bhuwana3, Vaishali Sah1, Vikramadiya Upmanyu4, A K Tiwari4, A P Sahoo5, A R Sahoo5, Sajjad A Wani5, Manjit Panigrahi1, N R Sahoo1, Ravi Kumar6.   

Abstract

In present investigation, differential expression of transcriptome after classical swine fever (CSF) vaccination has been explored at the cellular level in crossbred and indigenous (desi) piglets. RNA Sequencing by Expectation-Maximization (RSEM) package was used to quantify gene expression from RNA Sequencing data, and differentially expressed genes (DEGs) were identified using EBSeq, DESeq2, and edgeR softwares. After analysis, 5222, 6037, and 6210 common DEGs were identified in indigenous post-vaccinated verses pre-vaccinated, crossbred post-vaccinated verses pre-vaccinated, and post-vaccinated crossbred verses indigenous pigs, respectively. Functional annotation of these DEGs showed enrichment of antigen processing-cross presentation, B cell receptor signaling, T cell receptor signaling, NF-κB signaling, and TNF signaling pathways. The interaction network among the immune genes included more number of genes with greater connectivity in vaccinated crossbred than the indigenous piglets. Higher expression of IRF3, IL1β, TAP1, CSK, SLA2, SLADM, and NF-kB in crossbred piglets in comparison to indigenous explains the better humoral response observed in crossbred piglets. Here, we predicted that the processed CSFV antigen through the T cell receptor signaling cascade triggers the B cell receptor-signaling pathway to finally activate MAPK kinase and NF-κB signaling pathways in B cell. This activation results in expression of genes/transcription factors that lead to B cell ontogeny, auto immunity and immune response through antibody production. Further, immunologically important genes were validated by qRT-PCR.

Entities:  

Keywords:  CSF vaccination; Functional annotation; Humoral response; Pigs; Transcriptome

Mesh:

Substances:

Year:  2017        PMID: 28361233     DOI: 10.1007/s10142-017-0558-8

Source DB:  PubMed          Journal:  Funct Integr Genomics        ISSN: 1438-793X            Impact factor:   3.410


  34 in total

1.  An assessment of opportunities to dissect host genetic variation in resistance to infectious diseases in livestock.

Authors:  G Davies; S Genini; S C Bishop; E Giuffra
Journal:  Animal       Date:  2009-03       Impact factor: 3.240

2.  EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments.

Authors:  Ning Leng; John A Dawson; James A Thomson; Victor Ruotti; Anna I Rissman; Bart M G Smits; Jill D Haag; Michael N Gould; Ron M Stewart; Christina Kendziorski
Journal:  Bioinformatics       Date:  2013-02-21       Impact factor: 6.937

Review 3.  Signaling in innate immunity and inflammation.

Authors:  Kim Newton; Vishva M Dixit
Journal:  Cold Spring Harb Perspect Biol       Date:  2012-03-01       Impact factor: 10.005

4.  Inhibition of human cytomegalovirus replication by overexpression of CREB1.

Authors:  Yi Ling Chia; Chew Har Ng; Philip Lashmit; Kai Ling Chu; Qiao Jing Lew; Jia Pei Ho; Hsueh Lee Lim; Peter Morin Nissom; Mark F Stinski; Sheng-Hao Chao
Journal:  Antiviral Res       Date:  2013-12-05       Impact factor: 5.970

5.  Patterns of cellular gene expression in swine macrophages infected with highly virulent classical swine fever virus strain Brescia.

Authors:  Manuel V Borca; Ingigerdur Gudmundsdottir; Ignacio J Fernández-Sainz; Lauren G Holinka; Guillermo R Risatti
Journal:  Virus Res       Date:  2008-10-10       Impact factor: 3.303

6.  Differential expression analysis for sequence count data.

Authors:  Simon Anders; Wolfgang Huber
Journal:  Genome Biol       Date:  2010-10-27       Impact factor: 13.583

7.  A direct comparison of protein interaction confidence assignment schemes.

Authors:  Silpa Suthram; Tomer Shlomi; Eytan Ruppin; Roded Sharan; Trey Ideker
Journal:  BMC Bioinformatics       Date:  2006-07-26       Impact factor: 3.169

8.  Erratum to: A survey of best practices for RNA-seq data analysis.

Authors:  Ana Conesa; Pedro Madrigal; Sonia Tarazona; David Gomez-Cabrero; Alejandra Cervera; Andrew McPherson; Michal Wojciech Szcześniak; Daniel J Gaffney; Laura L Elo; Xuegong Zhang; Ali Mortazavi
Journal:  Genome Biol       Date:  2016-08-26       Impact factor: 13.583

9.  Transcriptome profile of the human placenta.

Authors:  Marta Majewska; Aleksandra Lipka; Lukasz Paukszto; Jan Pawel Jastrzebski; Kamil Myszczynski; Marek Gowkielewicz; Marcin Jozwik; Mariusz Krzysztof Majewski
Journal:  Funct Integr Genomics       Date:  2017-03-01       Impact factor: 3.410

10.  Understanding Haemophilus parasuis infection in porcine spleen through a transcriptomics approach.

Authors:  Hongbo Chen; Changchun Li; Mingdi Fang; Mengjin Zhu; Xinyun Li; Rui Zhou; Kui Li; Shuhong Zhao
Journal:  BMC Genomics       Date:  2009-02-05       Impact factor: 3.969

View more
  2 in total

1.  Genome-wide integrated analysis of miRNA and mRNA expression profiles to identify differentially expressed miR-22-5p and miR-27b-5p in response to classical swine fever vaccine virus.

Authors:  Lalrengpuii Sailo; Amit Kumar; Vaishali Sah; Rajni Chaudhary; Vikramaditya Upmanyu; A K Tiwari; Ajay Kumar; Aruna Pandey; Shikha Saxena; Akansha Singh; Sajad Ahmad Wani; Ravi Kumar Gandham; Anil Rai; B P Mishra; R K Singh
Journal:  Funct Integr Genomics       Date:  2019-05-28       Impact factor: 3.674

2.  Transcriptome profile analysis of leg muscle tissues between slow- and fast-growing chickens.

Authors:  Pengfei Wu; Guojun Dai; Fuxiang Chen; Lan Chen; Tao Zhang; Kaizhou Xie; Jinyu Wang; Genxi Zhang
Journal:  PLoS One       Date:  2018-11-07       Impact factor: 3.240

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.