Deciphering the combinatorial landscape of immunity

• Main Authors: Antonio Cappuccio, Shane T Jensen, Boris M Hartmann, Stuart C Sealfon, Vassili Soumelis, Elena Zaslavsky
• Format: Article
• Language: English
• Published: eLife Sciences Publications Ltd 2020-11-01
• Series: eLife
• Subjects: combination treatment experiment; cytokine interactions; synergy antagonism; dendritic cell T cell cross-talk; machine learning; signal integration
• Online Access: https://elifesciences.org/articles/62148

From cellular activation to drug combinations, immunological responses are shaped by the action of multiple stimuli. Synergistic and antagonistic interactions between stimuli play major roles in shaping immune processes. To understand combinatorial regulation, we present the immune Synergistic/Antagonistic Interaction Learner (iSAIL). iSAIL includes a machine learning classifier to map and interpret interactions, a curated compendium of immunological combination treatment datasets, and their global integration into a landscape of ~30,000 interactions. The landscape is mined to reveal combinatorial control of interleukins, checkpoints, and other immune modulators. The resource helps elucidate the modulation of a stimulus by interactions with other cofactors, showing that TNF has strikingly different effects depending on co-stimulators. We discover new functional synergies between TNF and IFNβ controlling dendritic cell-T cell crosstalk. Analysis of laboratory or public combination treatment studies with this user-friendly web-based resource will help resolve the complex role of interaction effects on immune processes.