Predicting position along a looping immune response trajectory

Abstract

When we get sick, we want to be resilient and recover our original health. To measure resilience, we need to quantify a host's position along its disease trajectory. Here we present Looper, a computational method to analyze longitudinally gathered datasets and identify gene pairs that form looping trajectories when plotted in the space described by these phases. These loops enable us to track where patients lie on a typical trajectory back to health. We analyzed two publicly available, longitudinal human microarray datasets that describe self-resolving immune responses. Looper identified looping gene pairs expressed by human donor monocytes stimulated by immune elicitors, and in YF17D-vaccinated individuals. Using loops derived from training data, we found that we could predict the time of perturbation in withheld test samples with accuracies of 94% in the human monocyte data, and 65–83% within the same cohort and in two independent cohorts of YF17D vaccinated individuals. We suggest that Looper will be useful in building maps of resilient immune processes across organisms.

Breeding for disease resilience: opportunities to manage polymicrobial challenge and improve commercial performance in the pig industry

“…However, novel analytical approaches to derive reliable descriptors that capture the trajectory characteristics and can be lent to routine genetic evaluation are required for implementing the dynamic trajectories in practical breeding programs (Doeschl-Wilson et al 2012a ; Knap and Doeschl-Wilson 2020 ). To date, the dynamic trajectory has been mainly described and used with well-controlled challenge tests targeting a specific pathogen, and its value to practical breeding in the pig industry is yet to be determined (Lough et al 2015 ; Rath et al 2018 ; Torres et al 2016 ).…”

Why breed disease-resilient livestock, and how?

“…In spite of these apparent advantages, implementation of such dynamic trajectories into practical breeding programs will require novel analytical approaches to derive and validate meaningful and reliable indicators that capture the trajectory characteristics and that lend themselves to routine genetic evaluation. Methods for analysing such complex trajectories, the patterns of which cannot be described by mathematical functions, have just started to emerge [110,[113][114][115][116], and as such the value to animal breeding is yet to be determined.…”