Dataset Information

Transcriptomics of chicken cecal tonsils and intestine after infection with low pathogenic avian influenza virus H9N2

ABSTRACT: Influenza viruses cause severe respiratory infections in humans and birds, triggering global health concerns and economic burden. Influenza infection is a dynamic process involving complex biological host responses. The objective of this study was to illustrate global biological processes in ileum and cecal tonsils at early time points after chickens were infected with low pathogenic avian influenza virus (LPAIV) H9N2 through transcriptome analysis. Total RNA isolated from ileum and cecal tonsils of non-infected and infected layers at 12-, 24- and 72-h post-infection (hpi) was used for mRNA sequencing analyses to characterize differentially expressed genes and overrepresented pathways. Statistical analysis highlighted transcriptomic signatures significantly occurring 24 and 72 hpi, but not earlier at 12 hpi. Interferon (IFN)-inducible and IFN-stimulated gene (ISG) expression was increased, followed by continued expression of various heat-shock proteins (HSP), including HSP60, HSP70, HSP90 and HSP110. Some upregulated genes involved in innate antiviral responses included DDX60, MX1, RSAD2 and CMPK2. The ISG15 antiviral mechanism pathway was highly enriched in ileum and cecal tonsils at 24 hpi. Overall, most affected pathways were related to interferon production and the heat-shock response. Research on these candidate genes and pathways is warranted to decipher underlying mechanisms of immunity against LPAIV in chickens.

SUBMITTER: Alqazlan N

PROVIDER: S-EPMC8517014 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:This study investigated the pathways of exposure to low pathogenic avian influenza (LPAI) virus among Australian commercial chicken farms and estimated the likelihood of this exposure occurring using scenario trees and a stochastic modeling approach following the World Organization for Animal Health methodology for risk assessment. Input values for the models were sourced from scientific literature and an on-farm survey conducted during 2015 and 2016 among Australian commercial chicken farms located in New South Wales and Queensland. Outputs from the models revealed that the probability of a first LPAI virus exposure to a chicken in an Australian commercial chicken farms from one wild bird at any point in time is extremely low. A comparative assessment revealed that across the five farm types (non-free-range meat chicken, free-range meat chicken, cage layer, barn layer, and free range layer farms), free-range layer farms had the highest probability of exposure (7.5?×?10-4; 5% and 95%, 5.7?×?10-4-0.001). The results indicate that the presence of a large number of wild birds on farm is required for exposure to occur across all farm types. The median probability of direct exposure was highest in free-range farm types (5.6?×?10-4 and 1.6?×?10-4 for free-range layer and free-range meat chicken farms, respectively) and indirect exposure was highest in non-free-range farm types (2.7?×?10-4, 2.0?×?10-4, and 1.9?×?10-4 for non-free-range meat chicken, cage layer, and barn layer farms, respectively). The probability of exposure was found to be lowest in summer for all farm types. Sensitivity analysis revealed that the proportion of waterfowl among wild birds on the farm, the presence of waterfowl in the range and feed storage areas, and the prevalence of LPAI in wild birds are the most influential parameters for the probability of Australian commercial chicken farms being exposed to avian influenza (AI) virus. These results highlight the importance of ensuring good biosecurity on farms to minimize the risk of exposure to AI virus and the importance of continuous surveillance of LPAI prevalence including subtypes in wild bird populations.

Dataset Information

Transcriptomics of chicken cecal tonsils and intestine after infection with low pathogenic avian influenza virus H9N2

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets