Network analysis of large phospho-signalling datasets: application to Plasmodium-erythrocyte interactions

Abstract

Phosphorylation based signalling is a complicated and intertwined series of pathways critical to all domains of life. This interconnectivity, though essential to life, makes understanding and decoding the interactions difficult. Large datasets of phosphorylation interactions through the activity of kinases on their numerous effectors are now being generated, however interpretation of the network environment remains challenging. In humans, many phosphorylation interactions have been identified across published works to form the known phosphorylation interaction network. We overlayed phosphorylation datasets onto this network which provided information to each of the connections. To analyse the datasets now mapped into a network, we designed a pathway analysis that uses random walks to identify chains of phosphorylation events occurring much more or much less frequently than expected. This analysis highlights pathways of phosphorylation that work synergistically, providing a rapid interpretation of the most critical pathways in a given dataset. Here we used datasets of human red blood cells infected with the notable stages of Plasmodium falciparum asexual development. The analysis identified several known signalling interactions, and additional interactions which could form the basis of numerous future studies. The network analysis designed here is widely applicable to any comparative phosphorylation dataset across infection and disease and can provide a rapid and reliable analysis to guide validation studies.