Gotia, H. T. | Search bioRxiv

bioRxiv2020

Capture-based enrichment of Theileria parva DNA enables full genome assembly of first buffalo- derived strain and reveals exceptional intra-specific genetic diversity

Theileria parva is an economically important, intracellular, tick-transmitted parasite of cattle. A live vaccine against the parasite is effective against challenge from cattle-transmissible T. parva but not against genotypes originating from the African Cape buffalo, a major wildlife reservoir, prompting the need to characterize genome-wide variation within and between cattle- and buffalo-associated T. parva populations. Here, we describe a capture-based target enrichment approach that enables, for the first time, de novo assembly of nearly complete T. parva genomes derived from infected host cell lines. This approach has exceptionally high specificity and sensitivity and is successful for both cattle- and buffalo-derived T. parva parasites. De novo genome assemblies generated for cattle genotypes differ from the reference by [~]54K single nucleotide polymorphisms (SNPs) throughout the 8.31 Mb genome, an average of 6.5 SNPs/kb. We report the first buffalo-derived T. parva genome, which is larger than the genome from the reference, cattle-derived, Muguga strain. The average non-synonymous nucleotide diversity ({pi}N) per gene, between buffalo-derived T. parva and the Muguga strain, was 1.3%. This remarkably high level of genetic divergence is supported by an average FST, genome-wide, of 0.44, reflecting a degree of genetic differentiation between cattle- and buffalo-derived T. parva parasites more commonly seen between, rather than within, species, with clear implications for vaccine development. The DNA capture approach used provides clear advantages over alternative T. parva DNA enrichment methods used previously and enables in-depth comparative genomics in this apicomplexan parasite.

genomics↗

bioRxiv2019

Re-annotation of the Theileria parva genome refines 53% of the proteome and uncovers essential components of N-glycosylation, a conserved pathway in many organisms

BackgroundThe apicomplexan parasite Theileria parva causes a livestock disease called East coast fever (ECF), with millions of animals are at risk in sub-Saharan East and Southern Africa, the geographic distribution of T. parva. Over a million bovines die each year of ECF, with a tremendous economic burden to pastoralists in endemic countries. Comprehensive, accurate parasite genome annotation can facilitate the discovery of novel chemotherapeutic targets for disease treatment, as well as elucidate the biology of the parasite. However, genome annotation remains a significant challenge because of limitations in the quality and quantity of the data being used to inform the location and function of protein-coding genes and, when RNA data are used, the underlying biological complexity of the processes involved in gene expression. Here, we apply our recently published RNAseq dataset derived from the schizont life-cycle stage of T. parva to update structural and functional gene annotations across the entire nuclear genome. ResultsThe re-annotation effort lead to evidence-supported updates in over half of all protein-coding sequence (CDS) predictions, including exon changes, gene merges and gene splitting, an increase in average CDS length of approximately 50 base pairs, and the identification of 128 new genes. Among the new genes identified were those involved in N-glycosylation, a process previously thought not to exist in this organism and a potentially new chemotherapeutic target pathway for treating ECF. Alternatively-spliced genes were identified, and antisense and multi-gene family transcription were extensively characterized. ConclusionsThe process of re-annotation led to novel insights into the organization and expression profiles of protein-coding sequences in this parasite, and uncovered a minimal N-glycosylation pathway that changes our current understanding of the evolution of this post-translation modification in apicomplexan parasites.

genomics↗