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Integrated time-serial transcriptome networks reveal common innate and tissue-specific adaptive immune responses to PRRSV infection.


ABSTRACT: Porcine reproductive and respiratory syndrome virus (PRRSV) infection is the most important viral disease causing severe economic losses in the swine industry. However, mechanisms underlying gene expression control in immunity-responsible tissues at different time points during PRRSV infection are poorly understood. We constructed an integrated gene co-expression network and identified tissue- and time-dependent biological mechanisms of PRRSV infection through bioinformatics analysis using three tissues (lungs, bronchial lymph nodes [BLNs], and tonsils) via RNA-Seq. Three groups with specific expression patterns (i.e., the 3-dpi, lung, and BLN groups) were discovered. The 3 dpi-specific group showed antiviral and innate-immune signalling similar to the case for influenza A infection. Moreover, we observed adaptive immune responses in the lung-specific group based on various cytokines, while the BLN-specific group showed down-regulated AMPK signalling related to viral replication. Our study may provide comprehensive insights into PRRSV infection, as well as useful information for vaccine development.

SUBMITTER: Lim B 

PROVIDER: S-EPMC7552595 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Integrated time-serial transcriptome networks reveal common innate and tissue-specific adaptive immune responses to PRRSV infection.

Lim Byeonghwi B   Kim Sangwook S   Lim Kyu-Sang KS   Jeong Chang-Gi CG   Kim Seung-Chai SC   Lee Sang-Myeong SM   Park Choi-Kyu CK   Te Pas Marinus F W MFW   Gho Haesu H   Kim Tae-Hun TH   Lee Kyung-Tai KT   Kim Won-Il WI   Kim Jun-Mo JM  

Veterinary research 20201013 1


Porcine reproductive and respiratory syndrome virus (PRRSV) infection is the most important viral disease causing severe economic losses in the swine industry. However, mechanisms underlying gene expression control in immunity-responsible tissues at different time points during PRRSV infection are poorly understood. We constructed an integrated gene co-expression network and identified tissue- and time-dependent biological mechanisms of PRRSV infection through bioinformatics analysis using three  ...[more]

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