Evidence of inflammatory immune signaling in chronic fatigue syndrome: A pilot study of gene expression in peripheral blood

Abstract

Background: Genomic profiling of peripheral blood reveals altered immunity in chronic fatigue syndrome (CFS) however interpretation remains challenging without immune demographic context. The object of this work is to identify modulation of specific immune functional components and restructuring of co-expression networks characteristic of CFS using the quantitative genomics of peripheral blood.

Methods: Gene sets were constructed a priori for CD4+ T cells, CD8+ T cells, CD19+ B cells, CD14+ monocytes and CD16+ neutrophils from published data. A group of 111 women were classified using empiric case definition (U.S. Centers for Disease Control and Prevention) and unsupervised latent cluster analysis (LCA). Microarray profiles of peripheral blood were analyzed for expression of leukocyte-specific gene sets and characteristic changes in co-expression identified from topological evaluation of linear correlation networks.

Results: Median expression for a set of 6 genes preferentially up-regulated in CD19+ B cells was significantly lower in CFS (p = 0.01) due mainly to PTPRK and TSPAN3 expression. Although no other gene set was differentially expressed at p < 0.05, patterns of co-expression in each group differed markedly. Significant co-expression of CD14+ monocyte with CD16+ neutrophil (p = 0.01) and CD19+ B cell sets (p = 0.00) characterized CFS and fatigue phenotype groups. Also in CFS was a significant negative correlation between CD8+ and both CD19+ up-regulated (p = 0.02) and NK gene sets (p = 0.08). These patterns were absent in controls.

Conclusion: Dissection of blood microarray profiles points to B cell dysfunction with coordinated immune activation supporting persistent inflammation and antibody-mediated NK cell modulation of T cell activity. This has clinical implications as the CD19+ genes identified could provide robust and biologically meaningful basis for the early detection and unambiguous phenotyping of CFS.

Figure 1

Cumulative probability plot of Δ differential expression of CFS versus NF for random gene sets similar in size to the CD19+ B cell up-regulated gene set and the NK cell gene set.

Figure 2

Box and whisker plot for the expression of each gene in the CD19+ up-regulated gene set in each of the 3 empiric illness classes. Boxes indicate the lower quartile, median and upper quartile values. Whiskers are located at extreme values within 1.5 times the inter-quartile range from the ends of each box. Outliers are displayed with a red '+'. Each plot is annotated with the null probability for the difference in median expression between the NF and CFS subject groups.

Figure 3

Box and whisker plot for the expression of the CD19+ up-regulated gene set in each of the 3 empiric illness classes. Boxes indicate the lower quartile, median and upper quartile values. Whiskers are located at extreme values within 1.5 times the inter-quartile range from the ends of each box. Outliers are displayed with a red '+'. The plot is annotated with the null probability for the difference in median expression between the NF and CFS subject groups.

Figure 4

Network size S defined as the sum of all network edge weights (Equation 3) and plotted as a function of cutoff p-value for the empiric NF and CFS classes.

Figure 5

Heat maps of gene set co-expression expressed as linear correlation coefficient r_{a, b} at cutoff significance p_a,b<0.10 (□) and at cutoff significance p_a,b <0.05 (●) for empiric classes for non-fatigued (NF) controls, insufficient fatigue symptoms (ISF) and CFS as well as for LCA control classes (LCA-0, 2) and for all LCA disease classes.