|PREEYANON, LIKIT - Michigan State University|
|BROWN, C. TITUS - Michigan State University|
Submitted to: Cytogenetics and Genome Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/19/2015
Publication Date: 7/14/2015
Publication URL: http://handle.nal.usda.gov/10113/61080
Citation: Preeyanon, L., Brown, C., Cheng, H.H. 2015. Transcriptome variation in response to Marek’s disease virus acute infection. Cytogenetics and Genome Research. 145:154-163.
Interpretive Summary: Improving genetic resistance is a desirable and sustainable control measure for Marek’s disease (MD), a T cell lymphoma of chickens caused by the highly oncogenic Marek’s disease virus (MDV). By sequencing the RNA from MD resistant and susceptible birds, we find that not only are there genes that are differentially expressed but show alternative splicing patterns. Combined with genome (DNA) sequence, we can account for some of the observed variation in gene expression patterns. This suggests that we can, in part, account for the genetic differences in disease incidence, which is an important first step in trying to connect sequence variation with variation in complex traits like disease resistance. Ultimately, similar types of efforts should allow for the precise prediction and generation of elite birds with superior genetic resistance to a variety of infectious diseases.
Technical Abstract: Marek’s disease (MD) is an economically significant chicken disease that affects the poultry industry worldwide with estimated annual cost of $2 billion [Morrow and Fehler, 2004]. The disease is caused by the highly oncogenic Marek’s disease virus (MDV), an alphaherpesvirus that induces T-cell lymphomas in susceptible birds. Vaccination is the primary control measure, which is effective in reducing incidence of tumor formation. However, since MD vaccines are not sterilizing, they do not prevent infection or horizontal spread of the virus. As a consequence, MDV field strains that overcome vaccinal protection have arisen repeatedly over time [Atkins et al., 2013] Therefore, there is a need for sustainable alternative control measures, such as improving genetic resistance. Many studies have reported strong associations between MHC alleles and resistance or susceptibility to MD. For example, chickens with the MHC allele B21 are resistant in contrast to chickens with the B19 allele, which are susceptible. ADOL lines 6 and 7, both share the same MHC B2 allele, yet exhibit different phenotypic responses; e.g., challenge with the JM/102W strain typically result in 0 and 100% MD incidence for lines 6 and 7, respectively. Thus, the major unanswered questions are what genetic factors, especially those that are non-MHC, contribute to susceptibility and resistance to the disease, and what are the main contributing mechanisms? In the past decades, significant efforts have been made to study variations in global gene expression between MD resistant and susceptible birds using microarray and RNA-Seq methods in order to identify non-MHC genes that contribute to resistance to MD [Vallejo et al., 1998; Bumstead, 1998; Yonash et al., 1999; Morgan et al., 2001; Sarson et al., 2008]. However, none of the studies have investigated differential expression of alternative isoforms, which are known to play a significant role in many biological events including immune responses [Lynch, 2004]. In addition, studies have shown that isoform expression levels can provide better signatures for some diseases [Zhang et al., 2013]. Changes of isoform expression levels are governed partly by two types of cis-regulatory elements: Exonic Splicing Enhancer (ESE) and Exonic Splicing Silencer (ESS), both located within an exon sequence. A number of sequence motifs of ESE and ESS have been identified in human and other organisms and can be predicted in silico. Mutations that disrupt or create those motifs could alter splicing patterns leading to aberrant alternative splicing. A number of disease-associated single-nucleotide polymorphisms (SNPs) in coding regions that affect ESEs and ESSs have been well characterized [Blencowe, 2000; Wang and Cooper, 2007]. Therefore, variations in isoform expression could lead to identification of SNPs that underlie genetic resistance to MD. Here we report differential-expressed genes and isoforms that may contribute to resistance to MD as well as SNPs that can potentially affect isoform expression levels.