Characterisation of retroviruses in the horse genome and their transcriptional activity via transcriptome sequencing.

The research aims to understand the presence and activity of endogenous retroviruses (ERVs) in the horse genome, with almost a thousand ERV gene sequences identified via data mining.

Research Objective and Methodology

• This study aimed to further understand and characterize the repetitive elements or endogenous retroviruses (ERVs) in the recently released draft horse genome. ERVs are viral sequences that have been integrated into the genome of an organism and passed on from generation to generation.
• The researchers identified these ERVs by using a data mining strategy, specifically using the proteins of the murine leukaemia virus as a reference or query. This approach resulted in the identification of a whopping 978 ERV gene sequences in the horse genome.
• The identified ERV sequences belonged to gamma, epsilon, and betaretrovirus genera, categories of retroviruses. At least one full length gammaretroviral locus was also found among these sequences, indicating the presence of complete genes that can potentially produce viruses.

Research Findings

• The researchers then studied the RNA expression of these ERVs from RNA transcriptome data derived from a variety of equine tissues. The RNA transcriptome data gives a snapshot of what genes are actively being transcribed or “expressed” at any particular moment.
• The results revealed that unlike in humans and mice, there don’t seem to be specific phylogenetic groups of equine ERVs that are more transcriptionally active. This contrasts with other well-studied organisms, where certain groups of ERVs are known to be more active than others.

Significance of Research

• This study is important as it not only identifies and characterizes the presence of ERVs in the horse genome, but also analyses their activity levels. This can help in understanding the potential impact of these ERVs on the horse’s health and evolution.
• Moreover, the method used in this study – data mining strategy using viral proteins as queries – has been shown to be a more technically feasible way to characterize ERV expression than previous methods. This could influence the future studies on ERV characterization in different organisms.