Tracking resilience to infections by mapping disease space

Individuals that recover from infections travel a path through disease space that returns to its origin. By following Plasmodium infected mice longitudinally from sickness to recovery, we show that these routes trace simple looping paths that can serve as maps. These maps can predict outcomes, reveal safe avenues and can help us avoid bad neighborhoods. Hosts that will suffer adverse effects enter regions of disease space that are not traversed by resilient individuals. Here we show that we can map disease outcomes using both longitudinal mouse data and cross-sectional human data. These maps identify relationships that could be useful in predicting and treating infections; they also show us that we commonly lack samples in regions of disease space that would help to build better predictive models. Total RNA was collected from tail nicks of female mice infected with Plasmodium chabaudi (10^5 iRBCs). Each mouse was monitored daily for 26 days. MIcroarray analysis was done on all 26 days for 3 surviving mice and for four mice that only survive up to 8-11 days after the infection (non-survivors). Control samples for two uninfectetd mice for days 0,8,10,14, & 24 are included.