Construction of disease-specific cytokine profiles by associating disease genes with immune responses

• Published in: PLoS computational biology Vol. 18; no. 4; p. e1009497
• Main Authors: Liu, Tianyun, Wang, Shiyin, Wornow, Michael, Altman, Russ B.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2022; Public Library of Science (PLoS)
• Subjects: Autoimmune diseases; Biology and Life Sciences; Chemokines; Computer and Information Sciences; Cytokines; Cytokines - metabolism; Feature extraction; Genes; Genetic aspects; Growth factors; Health aspects; Hormones; Humans; Immune response; Immune system; Immunity; Inflammation; Inflammation - genetics; Inflammatory diseases; Mathematical analysis; Medicine and Health Sciences; Metabolism; Pathogenesis; Pattern analysis; Protein interaction; Proteins; Strings; Tumor necrosis factor-TNF; United States
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1009497

The pathogenesis of many inflammatory diseases is a coordinated process involving metabolic dysfunctions and immune response—usually modulated by the production of cytokines and associated inflammatory molecules. In this work, we seek to understand how genes involved in pathogenesis which are often not associated with the immune system in an obvious way communicate with the immune system. We have embedded a network of human protein-protein interactions (PPI) from the STRING database with 14,707 human genes using feature learning that captures high confidence edges. We have found that our predicted Association Scores derived from the features extracted from STRING’s high confidence edges are useful for predicting novel connections between genes, thus enabling the construction of a full map of predicted associations for all possible pairs between 14,707 human genes. In particular, we analyzed the pattern of associations for 126 cytokines and found that the six patterns of cytokine interaction with human genes are consistent with their functional classifications. To define the disease-specific roles of cytokines we have collected gene sets for 11,944 diseases from DisGeNET. We used these gene sets to predict disease-specific gene associations with cytokines by calculating the normalized average Association Scores between disease-associated gene sets and the 126 cytokines; this creates a unique profile of inflammatory genes (both known and predicted) for each disease. We validated our predicted cytokine associations by comparing them to known associations for 171 diseases. The predicted cytokine profiles correlate (p-value<0.0003) with the known ones in 95 diseases. We further characterized the profiles of each disease by calculating an “Inflammation Score” that summarizes different modes of immune responses. Finally, by analyzing subnetworks formed between disease-specific pathogenesis genes, hormones, receptors, and cytokines, we identified the key genes responsible for interactions between pathogenesis and inflammatory responses. These genes and the corresponding cytokines used by different immune disorders suggest unique targets for drug discovery.