Identification of immune responsive protein epitopes in theileria parva and the tick vector rhipicephalus appendiculatus
Kigoni, Milcah Wagio
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Vector-borne diseases are some of the most widely distributed and poorly controlled diseases that affect both humans and livestock globally. East Coast Fever (ECF) is a tick-borne disease caused by the protozoan parasite Theileria parva, which is transmitted by the Rhipicephalus appendiculatus ticks. It is endemic in Eastern, Central and Southern Africa, where it kills at least 1 million cattle and causes economic losses exceeding 300 million USD annually. Current control methods such as chemotherapy, vector control using acaricides and immunization by Infection and Treatment Method (ItM) have not been effective in mitigating ECF. Although research is ongoing, an effective subunit vaccine against the disease is still elusive. Proteins facilitate most biological processes and their high tendency to be antigenic, makes them important targets for subunit vaccine development. One of the approaches to elucidate the function of such proteins is through protein-protein interaction studies, which are based on the 'guilty by association' principle. ECF vaccine research has mainly been focused on the hostparasite protein interaction. However, transmission of T parva is dependent on its ability to survive in the tick vector. Therefore, targeting parasite and vector antigenic proteins expressed during tick life cycle stages, may lead to discovery of potential ECF transmission-blocking vaccine candidates. This study, therefore, aimed to develop a computational model that could predict protein-protein interactions between T parva and its tick vector, so as to unravel proteins that are vital for vector and parasite survival. Also, the study predicted potential immune responsive epitopes in these proteins as targets for vaccine development. A machine learning approach, Support Vector Machine (SVM), was trained at an accuracy of 93.14%, to classify between interacting and non-interacting proteins. The model's performance was evaluated by the area under curve (AUC) of a receiver operative characteristics (ROC) curve. Immunoinformatics tools were used identify MBC class I and class II epitopes. A total of 9,917 proteinprotein interactions were predicted between the tick vector and parasite proteins at a prediction value range of 1-2.8, indicating possibility of very strong interaction. Subsequently, 261 and 1,479 epitopes were predicted in R. appendiculatus and T parva proteins respectively The functional domains identified in the interacting proteins, revealed that these proteins are involved in numerous cellular functions that are important for parasite and vector survival. Additionally, literature showed that most tick proteins involved in the interactions are known anti-tick vaccine candidates, but none have been studied as transmission blocking vaccines. The study concluded that protein interaction occurs between parasite and vector proteins that play key cellular functions for the survival ECF parasite and vector. Further, that the interacting proteins have epitopes with potential to induce a protective immune response when used to vaccinate the cattle. Hence, uptake of immune response components during tick feeding may inhibit protein function, which in turn affects parasite and vector survival. These proteins are therefore good targets for a cocktail transmission blocking vaccine against East Coast Fever but appropriate screening procedures are needed to validate the best candidates. This approach offers the advantage of controlling both tick numbers and disrupting the tick vector-pathogen interface therefore blocking ECF transmission to the bovine host.