Use of Graph Theory to Characterize Human and Arthropod Vector Cell Protein Response to Infection With .

One of the major challenges in modern biology is the use of large omics datasets for the characterization of complex processes such as cell response to infection. These challenges are even bigger when analyses need to be performed for comparison of different species including model and non-model organisms. To address these challenges, the graph theory was applied to characterize the tick vector and human cell protein response to infection with , the causative agent of human granulocytic anaplasmosis. A network of interacting proteins and cell processes clustered in biological pathways, and ranked with indexes representing the topology of the proteome was prepared. The results demonstrated that networks of functionally interacting proteins represented in both infected and uninfected cells can describe the complete set of host cell processes and metabolic pathways, providing a deeper view of the comparative host cell response to pathogen infection. The results demonstrated that changes in the tick proteome were driven by modifications in protein representation in response to infection. Pathogen infection had a higher impact on tick than human proteome. Since most proteins were linked to several cell processes, the changes in protein representation affected simultaneously different biological pathways. The method allowed discerning cell processes that were affected by pathogen infection from those that remained unaffected. The results supported that human neutrophils but not tick cells limit pathogen infection through differential representation of ras-related proteins. This methodological approach could be applied to other host-pathogen models to identify host derived key proteins in response to infection that may be used to develop novel control strategies for arthropod-borne pathogens.