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

Chronic fatigue syndrome (CFS) is estimated to cost the American economy over $9 billion each year in lost productivity [1]. Among other components chronic immune cell dysfunction and activation has been demonstrated in CFS by several groups [2–4]. Though similar in terms of broad lymphocyte classes CFS and non-fatigued subjects can be readily distinguished when specific immune cell subsets are examined. For example Klimas et al. [2] report a significant expansion CD26+ (DPP-IV) activated T cells in CFS subjects. This multifunctional molecule plays a major role in the regulation, development, maturation and migration of T helper (Th) and natural killer (NK) cells as well as in B cell immunoglobulin switching [5]. Moreover abnormal expression of CD26+ is found in autoimmune diseases [6]. More recently CFS patients were also reported to have significantly fewer CD3+/CD25- T cells and significantly more CD20+/CD5+ B cells [7], a subset associated with auto-antibodies. Significantly fewer CD56+ NK cells were also observed in recent work by Racciatti et al. [8]. Though important, flow cytometry results such as these leave many questions regarding cellular state unanswered. Microarray profiling of gene expression on the other hand offers a glimpse of pathway activation in disease pathogenesis at molecular resolution. Microarray analysis of cDNA profiles in peripheral blood mononuclear cells (PBMC) have revealed altered expression in CFS of several immune genes [9, 10] involved in response to oxidative stress, NK cell activity and elements of antigen processing. Instability in immune response and restructuring of immune cell signaling under exercise challenge has also been observed [11]. Unfortunately microarray profiling is commonly performed on mixed cell populations producing an average profile from which it is very difficult to dissect the contributions of relative cell abundance, cell activation state and cell-cell signaling. More importantly, this averaging can obscure significant changes in the state of minority cell sub-populations.

These challenges notwithstanding, a review of this evidence strongly suggests that CFS pathogenesis is likely to include a characteristic immunologic component in at least one subset of the patient population [12]. However the exact nature of this immunologic component remains the object of considerable debate at least in part because of an inability to cast gene expression profiles in the useful context of immune cell demographics. In an attempt to address this issue methods have been proposed to dissect global gene expression profiles into discrete elements assignable to biologic processes [13–15]. The assignment of genes to discrete modules or sets has been successful in several respects. A first contribution involves simply reducing the dimensionality of >55,000 gene expression measures to that of say 10 or so gene sets. The interpretability of results is further enhanced by associating sets with basic cellular functions. Finally the numerical robustness is greatly improved through the averaging of changes in expression over many genes. In addition gene sets are transportable across microarray platforms making it possible to compare studies based on different technologies.

In this work we explore the use of discrete gene sets in extracting useful information regarding immune dysfunction in CFS from gene expression profiles of mixed lymphocyte populations. In particular we construct gene sets that capture elements of abundance and activity assignable to specific immune cell subsets thereby facilitating direct integration with flow cytometry results. Data from a large population-based study of CFS [16] is then examined for changes of immune set expression across two separate CFS classification approaches. In addition, patterns of coordinated expression linking these immune sets were investigated using simple correlation networks. These networks were examined for shifts in topology and point to patterns of immune signaling in CFS that are consistent with chronic inflammation. These observations could constitute a signature of CFS or a component thereof.

In this work we dissected PBMC gene expression profiles into components that were preferentially expressed in several isolated lymphocyte subpopulations. We also used 2 systems to stratify subjects into illness groups. The LCA class structure was inferred directly from a comprehensive set of clinical and biological indicators. All indicators were equally weighted and contrary to common practice no subset was assigned greater relevance a priori. In contrast the empiric classification which was based on a consensus of opinions from expert clinicians. Results confirm strong links between both systems with the LCA classification providing additional insight into potential subclasses of CFS. The commonalities between these classification systems are readily observed in the patterns of gene set co-expression. Indeed the empiric CFS group seems to present an aggregation of the gene set co-expression patterns observed in LCA classes 1 and 5. However, the differential expression of gene sets only achieves statistical significance in the case of the coarser empiric classes with the larger group sizes providing better noise reduction. Specifically in the empiric CFS class we found a significant decrease in the median expression for a set of 6 genes preferentially up-regulated in isolated CD19+ B cells compared to non-fatigued controls. Expression of this CD19+ B cell up-regulated gene set also discriminated ISF from controls at 0.05 confidence level. In a recent study of CFS occurrence both in the presence and absence of viral infection Racciati et al. [8] found no significant differences in CD19+ cell abundance. Robertson et al. [7] recently reported significantly higher abundance of CD20+/CD5+ B cells, a subset associated with the production of auto-antibodies, in patients with depression. These findings together with our observations of depressed CD19+ gene expression and altered association between up and down-regulated B cell functions would suggest that the function of these cells might be compromised in CFS subjects. Cole et al. [28] reported a selective reduction of mature B lymphocyte function in subjects who experienced chronic high levels of social isolation including suppression of several transcription factors involved B cell differentiation such as Ikaros/ZNF1A1. Genes encoding for members of the zinc finger protein family were also identified in previous work by this group as prominent contributors to the CFS symptom space [9]. A closer look at the 6 genes that constitute the CD19+ up-regulated set showed that the PTPRK and TSPAN3 genes, both associated with immune cell adhesion and development, were the most suppressed. Down-regulation of PTPRK, a TGF-β target gene, is known to be down-regulated by the Epstein-Barr virus (EBV) [29], an infectious agent known to trigger CFS [30, 31]. Down-regulation of TGF-β has been reported in CFS by Tomoda et al. [32].

NK cell activity is suppressed in CFS [33] and this decreased cytotoxity has been associated with reduced intracellular perforin [34]. In this work we observe an increased expression of the NK cell gene set. Of the 4 genes used to capture NK cell function the expression of NKG2A/C (NM 002260) was most increased. The binding of NKG2A to its natural ligand, human non-classic class I leukocyte antigen (HLA) E, is known to induce its immunoreceptor tyrosine-based inhibition motif (ITIM) and suppress cytotoxic cell effector activity [35]. Moreover NKG2A is also known to be co-expressed on activated Th2 but not Th1 lymphocytes [36]. A bias towards Th2-type immune response in CFS patients has also been suggested on the basis of intracellular T cell cytokine profiles by Skowera et al. [37]. Interestingly this also aligns with altered expression of the PTPRK gene mentioned above as Asano et al. [38] report impaired Th1 function with PTPRK deletion in rats. Therefore our observations supported findings of increased suppression of cytotoxic activity in CFS and hinted at increased Th2 activity though the latter were not specifically addressed in this analysis.

Neutrophils for their part are only found at trace and contaminating amounts in most PBMC preparations [39] so it is interesting to note that the neutrophil gene set arose as a core element in the emergence of coordinated immune activity. In particular the CD16+ neutrophil gene set and the CD14+ monocyte gene set shared significant co-expression. Not only do these arise from the same hematopoietic CD34+ progenitor cell [40] but since the immune community is highly integrated the presence or absence of neutrophils will also be mirrored in the state of the remaining cell population. The CD14+ monocyte set also shared significant co-expression with the CD19+ B cell gene sets. Together this neutrophil-monocyte-B cell immune interaction triad is highly consistent with a model of chronic inflammation proposed by Lefkowitz and Lefkowitz [41]. According to this model once an event initiates inflammation, neutrophils are among the first cells to arrive at the site. They degranulate releasing MPO into the microenvironment which together with iMPO binds to macrophage MMR receptor and induces release of TNF-α. The latter functions in an autocrine manner and along with iMPO initiates a cytokine cascade (IL-1, IL-6, IL-8, GM-CSF). IL-8 attracts more neutrophils and together with GM-CSF causes these to once again degranulate. With the corresponding release of additional MPO, the cycle starts once again. The TNF-α initiated cascade induces IL-6 which is used by B cells for maximum antibody secretion usually IgM. In addition to the present analysis, a preliminary examination of cytokine data collected in the Wichita study pointed to an increase in TNF-α in CFS subjects (data not shown) as documented previously by Moss et al. [42].

In addition to this core network, we also observed that CD8+ T cell set expression correlated negatively with that of the NK and CD19+ up-regulated B cell sets. In one possible mechanism linking these three cell types, IgG antibodies binding to GD3 on the surface of CD4+ and CD8+ T cells could elicit signals for proliferation of these subsets and expression of the IL-2 receptor CD25. NK cells have been shown to selectively inhibit this antibody-mediated proliferation of CD8+ T cells by Claus et al. [43] perhaps through down-regulation of autologous mixed lymphocyte reaction (MLR). This basic analysis of immune gene set co-expression points therefore to the existence of immune signaling processes in CFS that adhere to at least one known mechanism of chronic inflammation and support possible antibody-mediated NK cell modulation of T cell activity. Furthermore association networks constructed for LCA classes 1 and 5 suggested that B cell involvement in these processes may serve as factor for discriminating between distinct subsets of CFS subjects.

Although 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.