A formal analysis of cytokine networks in chronic fatigue syndrome

Abstract

Chronic Fatigue Syndrome (CFS) is a complex illness affecting 4 million Americans for which no characteristic lesion has been identified. Instead of searching for a deficiency in any single marker, we propose that CFS is associated with a profound imbalance in the regulation of immune function forcing a departure from standard pre-programmed responses. To identify these imbalances we apply network analysis to the co-expression of 16 cytokines in CFS subjects and healthy controls. Concentrations of IL-1a, 1b, 2, 4, 5, 6, 8, 10, 12, 13, 15, 17 and 23, IFN-γ, lymphotoxin-α (LT-α) and TNF-α were measured in the plasma of 40 female CFS and 59 case-matched controls. Cytokine co-expression networks were constructed from the pair-wise mutual information (MI) patterns found within each subject group. These networks differed in topology significantly more than expected by chance with the CFS network being more hub-like in design. Analysis of local modularity isolated statistically distinct cytokine communities recognizable as pre-programmed immune functional components. These showed highly attenuated Th1 and Th17 immune responses in CFS. High Th2 marker expression but weak interaction patterns pointed to an established Th2 inflammatory milieu. Similarly, altered associations in CFS provided indirect evidence of diminished NK cell responsiveness to IL-12 and LT-α stimulus. These observations are consistent with several processes active in latent viral infection and would not have been uncovered by assessing marker expression alone. Furthermore this analysis identifies key sub-networks such as IL-2:IFN-γ:TNF-α that might be targeted in restoring normal immune function.

Fig. 1

Networks for HC and CFS have visibly different topologies. A weighted spring-electrical embedding structurally reveals the subject-subject (inset) and cytokine-cytokine associations based on measurements in 59 healthy control subjects (a) and 40 CFS patients (b). All edge weights are significant at p ≤ 0.01. Separation of subjects was consistent with their assignment to diagnostic groups supporting the use of within-group variation in the estimation of mutual information for cytokine-cytokine associations.

Fig. 2

Most cytokines significantly modified their connectivity in the CFS state. Theses network alterations were revealed by the relative change in the total weight of edges connected at each node (node degree centrality) as well as edges acquired through first neighbors (normalized eigenvector centrality). Interleukins (IL), 2, 4, and 1b, interferon-gamma (IFNγ), and tumor necrosis factor-alpha (TNFα) became much better integrated into the core network in CFS, while interleukins, 5, 6, 12, 13, and 17 became more weakly associated.

Fig. 3

Both HC and CFS networks are composed of two distinct communities. Visually “relaxing” the links between identified communities of nodes and allowing them to drift apart emphasizes community structure in both networks. Overall modularity was maximized when each network was separated into two communities with differing compositions, labeled I+ at the top and II−. Each community represents a clustering of nodes with a greater internal linkage than would be expected compared to a random sampling of similar nodes.