Unique Mitochondrial Single Nucleotide Polymorphisms Demonstrate Resolution Potential to Discriminate Theileria parva Vaccine and Buffalo-Derived Strains

Distinct pathogenic and epidemiological features underlie different Theileria parva strains resulting in different clinical manifestations of East Coast Fever and Corridor Disease in susceptible cattle. Unclear delineation of these strains limits the control of these diseases in endemic areas. Hence, an accurate characterization of strains can improve the treatment and prevention approaches as well as investigate their origin. Here, we describe a set of single nucleotide polymorphisms (SNPs) based on 13 near-complete mitogenomes of T. parva strains originating from East and Southern Africa, including the live vaccine stock strains. We identified 11 SNPs that are non-preferentially distributed within the coding and non-coding regions, all of which are synonymous except for two within the cytochrome b gene of buffalo-derived strains. Our analysis ascertains haplotype-specific mutations that segregate the different vaccine and the buffalo-derived strains except T. parva-Muguga and Serengeti-transformed strains suggesting a shared lineage between the latter two vaccine strains. Phylogenetic analyses including the mitogenomes of other Theileria species: T. annulata, T. taurotragi, and T. lestoquardi, with the latter two sequenced in this study for the first time, were congruent with nuclear-encoded genes. Importantly, we describe seven T. parva haplotypes characterized by synonymous SNPs and parsimony-informative characters with the other three transforming species mitogenomes. We anticipate that tracking T. parva mitochondrial haplotypes from this study will provide insight into the parasite’s epidemiological dynamics and underpin current control efforts

Population dynamics of ticks infesting sheep in the arid steppes of Tunisia

This study aimed to determine tick population dynamics infesting sheep in Gafsa region (Central Tunisia). Ticks were collected monthly over a year, from October 2013 to September 2014, from 57‒64 randomly-included Barbarine-breed sheep. In total, 560 ticks were collected and identified. They belonged to two species: Rhipicephalus sanguineus sensu lato (98.6%) and Hyalomma excavatum (1.4%). Sheep were only infested from April to October with a maximum infestation prevalence (number of infested animals / number of examined animals) in August for R. sanguineus s.l. (83%), and in May for H. excavatum (7%). The highest infestation intensity (number of ticks / number of infested sheep) was 3.7 ticks per animal in August. These results should help sheep owners and veterinarians to implement efficient control programs against ticks and the pathogens they transmit

Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1900548116/-/DCSupplemental.Copyright © 2019 The Author(s). One of the most challenging tasks in modern science is the development of systems biology models: Existing models are often very complex but generally have low predictive performance. The construction of high-fidelity models will require hundreds/thousands of cycles of model improvement, yet few current systems biology research studies complete even a single cycle. We combined multiple software tools with integrated laboratory robotics to execute three cycles of model improvement of the prototypical eukaryotic cellular transformation, the yeast (Saccharomyces cerevisiae) diauxic shift. In the first cycle, a model outperforming the best previous diauxic shift model was developed using bioinformatic and systems biology tools. In the second cycle, the model was further improved using automatically planned experiments. In the third cycle, hypothesis-led experiments improved the model to a greater extent than achieved using high-throughput experiments. All of the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for automatic execution, and the results stored on the semantic web for reuse. The final model adds a substantial amount of knowledge about the yeast diauxic shift: 92 genes (+45%), and 1,048 interactions (+147%). This knowledge is also relevant to understanding cancer, the immune system, and aging. We conclude that systems biology software tools can be combined and integrated with laboratory robots in closed-loop cycles.HIST-ERA AdaLab project: The Engineering and Physical Sciences Research Council (EPSRC), UK(EP/M015661/1) ANR-14-CHR2-0001-01