Predicting position along a looping immune response trajectory.

Area of Science:

• Immunology
• Computational Biology
• Genomics

Background:

• Host resilience and disease recovery are crucial aspects of health.
• Quantifying a host's position on a disease trajectory is essential for measuring resilience.
• Longitudinal datasets offer valuable insights into dynamic biological processes like immune responses.

Purpose of the Study:

• To introduce Looper, a novel computational method for analyzing longitudinal data.
• To identify gene pairs exhibiting looping trajectories indicative of disease recovery.
• To enable precise tracking of patients along their trajectory back to health.

Main Methods:

• Looper analyzes longitudinally gathered datasets to identify gene pairs forming looping trajectories.
• The method plots gene pairs in a phase space defined by disease trajectory.
• Two publicly available, longitudinal human microarray datasets of self-resolving immune responses were analyzed.

Main Results:

• Looper identified looping gene pairs in human monocytes stimulated by immune elicitors.
• Looping gene pairs were also identified in individuals vaccinated with YF17D.
• The method predicted perturbation time in withheld samples with high accuracy (94% for monocyte data, 65-83% for YF17D data).

Conclusions:

• Looper provides a robust method for quantifying host resilience and tracking disease recovery.
• The identified looping gene pairs serve as biomarkers for immune process dynamics.
• Looper is a valuable tool for mapping resilient immune processes across different organisms and conditions.