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 coexpression 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.
Conclusions: 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.
Final ParagraphAlthough several very plausible immune response mechanisms were recovered by this analysis it must be emphasized that the use of discrete gene sets has several limitations. In particular it becomes increasingly difficult to identify genes that are exclusively or even predominantly expressed in specific cell lineages when these share many commonalities of function and goal. This issue was reflected in by the small size of the gene sets identified in this work from lymphocyte subset expression profiles. An approach that promises to be more robust and more revealing still involves the direct use of the genome-wide expression for these cell populations. This remains an active area of research [44]. However, even this simple analysis points to dramatic differences in immune network topology and cell signaling in CFS and we expect these differences to be largely conserved in more elaborate analyses. Furthermore the methodology outlined and issues raised in this work demonstrate the importance of developing approaches that effectively integrate flow cytometry with cytokine and gene expression profiling. In particular it underscores the importance of looking beyond differential expression of individual components towards changes in their patterns of coordinated activity and formally recognizing the network properties of the immune system.
http://www.behavioralandbrainfunctions.com/content/4/1/44