|Maceachern, Sean -|
|Muir, William -|
|Crosby, Seth -|
Submitted to: Frontiers in Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 30, 2011
Publication Date: January 13, 2012
Citation: Maceachern, S., Muir, W.M., Crosby, S.D., Cheng, H.H. 2012. Genome-wide identification and quantification of cis- and trans-regulated genes responding to Marek's disease virus infection via analysis of allele-specific expression. Frontiers in Genetics. 2(113):1-11. Interpretive Summary: Marek’s disease, a T-cell lymphoma caused by an infectious herpesvirus, is one of the most serious diseases for the poultry industry. Understanding the genetic basis for resistance to Marek's and applying this information to enhance disease resistance is a critical and long-term goal of both the academic and industrial communities. In this study, we implement a novel strategy that incorporates ultra high throughput RNA sequencing to identify all the individual genes that respond to virus infection. This genes found in this study can be further evaluated in commercial populations. More importantly, this strategy can be applied to all other animal species to improve any agronomically-important trait.
Technical Abstract: Background Marek’s disease (MD) is a commercially important neoplastic disease of chickens caused by the Marek’s disease virus (MDV), a naturally-occurring oncogenic alphaherpesvirus. We attempted to identify genes conferring MD resistance, by completing a genome-wide screen for allele-specific expression (ASE) amongst F1 progeny of two inbred chicken lines that differ in MD resistance. High throughput sequencing was used to profile transcriptomes from pools of uninfected and infected individuals at 4 days post-infection (dpi) to identify all cSNPs and genes showing an allele-specific response to MDV infection. Illumina GoldenGate assays were subsequently used to quantify regulatory variation controlled at the gene (cis) and elsewhere in the genome (trans), by examining differences in expression between F1 individuals and artificial F1 RNA pools over 6 time periods. Results RNA sequencing identified 22,655 high confidence cSNPs of which 5,360 cSNPs in 3,773 genes exhibited significant allelic imbalance. Poor correlations between expression levels measured by RNA sequencing and GoldenGate assays were identified. However, allelic imbalance was confirmed in 1,184 of 1,233 the GoldenGate assays examined. Infection impacted ASE in these genes over time between infected and uninfected individuals at all time points. Large proportions of genes showing a response to infection at 1 and 11 dpi were also found, which may coincide with the various stages of viral infection. Changes in the number of genes showing transcriptional variation controlled by cis-, trans-, or a combination of both factors were identified with a general decrease in the proportion of cis-regulation and an increase in trans-regulation with increasing time following infection. Conclusions ASE analyses appear to be a powerful approach to identify regulatory variation responsible for differences in transcript abundance. In particular, the ability to quantify the amount of cis- and trans-acting variation and identify specific genes responsible for regulating transcript abundance underlying phenotypic differences in a complex trait could be investigated further to help identify causative polymorphisms and genetic mechanisms for MD resistance. The methods used here for identifying specific genes and alleles may have practical implications for applying marker-assisted selection to all complex traits that are difficult to measure in agricultural species.