Abstract

Perturbations in immune processes are a hallmark of a number of autoimmune and inflammatory disorders. Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is an inflammatory disorder with possible autoimmune correlates, characterized by reduced NK cell activity, elevations in regulatory T cells (Tregs) and dysregulation in cytokine levels. The purpose of this article is to examine innate and adaptive immune cell phenotypes and functional characteristics that have not been previously examined in CFS/ME patients. Thirty patients with CFS/ME and 25 non-fatigued controls were recruited for this study. Whole blood samples were collected from all participants for the assessment of cell phenotypes, functional properties, receptors, adhesion molecules, antigens and intracellular proteins using flow cytometric protocols. The cells investigated included NK cells, dendritic cells, neutrophils, B cells, T cells, γδT cells and Tregs. Significant changes were observed in B-cell subsets, Tregs, CD4+CD73+CD39+ T cells, cytotoxic activity, granzyme B, neutrophil antigens, TNF-α and IFN-γ in the CFS/ME patients in comparison with the non-fatigued controls. Alterations in B cells, Tregs, NK cells and neutrophils suggest significant impairments in immune regulation in CFS/ME and these may have similarities to a number of autoimmune disorders.

Introduction

Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) remains a controversial disorder in research and clinical medicine. Persistent debilitating fatigue and frequent episodes of flu-like symptoms that are not alleviated by rest or medication are among the hallmarks of this enervating disorder (1). Although some therapies are suggested to be successful in some patients, the vast majority of patients do not benefit from clinical interventions (2–7). Currently, six classification systems may be used to identify individuals as having CFS/ME and patients may be grouped into different subgroups based on the severity or incapacitating nature of their symptoms (1, 8–10). In spite of exhaustive research in recent decades, the cause(s) of CFS/ME is not yet completely understood, while improved prognostic indicators, more-efficient targeted treatment and better coping approaches continue to be elusive.

Coordination between the innate and adaptive immune system is necessary for health and wellness as it ensures cell survival, maturation, proliferation and functionality. Disruptions in these synchronized processes are hallmarks of most autoimmune and inflammatory disorders and may include decreases in NK cell lysis, shifts in cytokine profile, proliferation in certain T cells and abnormal cell functions (11). Failures in cellular processes significantly affect physiological homeostasis and have been confirmed in a number of CFS/ME patients (12–14).

Importantly, in CFS/ME, reports on basic immune cell numbers are inconsistent and this may be related to a number of factors including the severity and cyclical nature of the disorder. Nonetheless, reduced NK cytotoxic activity remains a consistent finding in most CFS/ME patients, perhaps suggesting a possible breakdown in the cytotoxic mechanism of these cells in CFS/ME. Reductions in NK cell function and significant increases in regulatory T cells (Tregs) in CFS/ME patients (14) may suggest an increase in Treg suppression, which may explain the reduced cytotoxic ability of the NK cells in CFS/ME patients. Similarly, heightened Treg levels may substantially suppress antigen-presenting cells by binding to co-receptors CD80 and CD86 and MHCII molecules on the surface of dendritic cells (DCs) via ligands including cytotoxic T lymphocyte antigen-4 (CTLA-4) and lymphocyte activation gene-3 (LAG-3), respectively (15). The suppressive functions of Tregs on DCs may be important in CFS/ME as a lack of mature DCs may affect the antigen-presenting function of DCs, particularly altering cytokine secretion by T helper (Th) cells (16). Interestingly, co-stimulatory molecules such as CD86 have been shown to be either decreased or increased on the surfaces of DCs in certain diseases (17, 18). In CFS/ME, little is known about the role of DCs. Given that DCs are central to a collapse in immune tolerance, it is imperative to determine their role in CFS/ME. DCs are known to incite the generation of autoreactive immune responses resulting in the generation of pathogenic autoantibodies and chronic inflammation (19–21).

DCs also interact with other antigen-presenting cells including γδT cells and this is a potent mechanism for CD86 expression (22). In the periphery, γδT cells represent about 1–10% of lymphocytes and 2–5% of the total T-cell populace (23, 24). In humans, there are two subsets of γδT cells, Vδ1 and Vδ2. Vδ1 is predominant in the spleen and thymus, while Vδ2 is abundant in the periphery, skin, tonsils and lymph nodes (25, 26). γδT cells are functionally similar to cytotoxic cells, NK cells and CD8+T cells (27). These cells also secrete cytokines that inhibit CD4+T-cell proliferation, induce apoptosis and promote B-cell antibodies and have been implicated in a number of diseases (28–30), hence, they may have a role in CFS/ME. Peripheral γδT cells may decrease in the presence of high levels of FOXP3 (31). Direct confirmation of the role of these cells in regulating immune mechanisms in CFS/ME remains to be determined. Similarly, there are no reports on the role of human neutrophil antigens (HNAs) in CFS/ME patients. This is the first study to investigate the potential role of innate and adaptive immune cell phenotypes and functional proteins of γδT cells, DCs and HNAs in the CFS/ME symptom profile.

Methods

Participant recruitment

Thirty CFS/ME patients (mean age = 51.15±1.92 years) and 25 non-fatigued controls (mean age = 50.42±1.76 years) were recruited for this study. Prior to participation in the study, each participant provided informed consent. All participants completed a questionnaire based on the Centre for Disease Prevention and Control (CDC 1994) criteria for CFS/ME (1, 8). CFS/ME patients qualified for the study upon meeting the CDC 1994 criteria for CFS/ME, while the non-fatigued group had no incidence of fatigue. An exclusionary criterion was pertinent to individuals with autoimmune disorders, psychosis, epilepsy, cardiac related disorders, pregnant or breastfeeding. Approval for this study was obtained from the Griffith University Human Research Ethics Committee.

Sample collection

Whole blood was collected from the antecubital vein of all participants into EDTA and lithium heparinized tubes. Routine full blood counts were performed to provide an indication of the levels of red blood cells, lymphocytes, granulocytes, monocytes and other blood parameters, using the Coulter ACT diff Analyzer (Beckman Coulter, High Wycombe, UK).

Cell phenotyping studies

All cell phenotyping studies were performed using whole blood samples. Cell phenotyping studies were performed for the following cells: DCs [plasmacytoid (pDCs), myeloid (mDCs) and CD16+ DCs], NK cells (CD56dim and CD56bright), B cells (immature, memory and plasma), γδT cells (naive, central memory, effector memory and effector memory CD45RA), Tregs (FOXP3+Tregs) and CD4T cells (CD4+CD73+CD39, CD4+CD73+CD39+, and CD4+CD73CD39+). Immunoflu orescent antibody staining was used to determine the phenotypes of each cell. The staining procedures were executed as previously described (32, 33). The mAbs (BD Biosciences, San Jose, CA, USA) used in determining cell phenotypes of interest included those recognizing CD3, CD19, CD21, CD20, CD27, HLA-DR, CD123, CD33, CD16, CD11c, CD62L, CD27, CD45RA, CD127, CD4, CD25, CD73, CD39, CD56, CD80, CD86, CD56, CD38, CD138 or CD10 (32, 33).

Assessment of surface antigens, adhesion molecules and receptors

Neutrophils were assessed based on the expression of HNAs including HNA-1 (CD16b), HNA-2 (CD177), HNA-4 (CD11b) and HNA-5 (CD11a). Whole blood samples were labelled with combinations of antibodies recognizing CD11a, CD62L, CD11b, CD16b, CD66abcd or CD177. NK receptors were measured following negative isolation of NK cells from whole blood using Rosettesep (StemCell Technologies, Vancouver, Canada). Isolated cells were then stained with CD3, CD16, CD56 and mAbs for CD158a/h (KIR2DL1/S1), CD158e (KIR3DL1), CD158b (KIR2DL2/DL3), CD158i (KIR2DS4) (Miltenyi Biotech, Bergisch Glabach, Germany) or NKG2D (BD Biosciences) (11). The migratory potential of γδT cells was examined using the CD11a and CD62L markers, while CD94 determined MHC recognition. Expression of co-stimulatory receptors CD80 and CD86 was used as a determinant of DC function. This was performed as previously described (34). Heparinized whole blood was diluted with RPMI at a ratio of 1:2 and stimulated with or without PMA (5ng/ml)/ionomycin (1 μg/ml) and incubated for 6h at 37°C with 5% CO2. Samples were then labelled with anti-HLA-DR, lineage cocktail 2 (CD3, CD14, CD19, CD20, CD56) and either CD80 or CD86 (BD Biosciences). Subsequently, samples were lysed, washed and fixed. All cells were then analysed on the flow cytometer.

Intracellular staining of functional proteins

Examination of NK proteins and FOXP3 was performed via intracellular staining (35). Briefly, PBMCs (1×107 cells/ml) were stained and incubated with fluorochrome-conjugated mAbs CD56, CD16, CD3, CD25, CD127 and CD4 (BD Biosciences). Samples were washed, resuspended and incubated in BD Cytofix (BD Biosciences) for 30min, following which they were washed in diluted perm wash buffer (BD Biosciences). mAbs for the proteins of interest, perforin, granzyme A (GZA), granzyme B (GZB) and FOXP3 were then added to the appropriate samples, incubated, washed and analysed on the flow cytometer.

NK cell cytotoxic activity, degranulation and IFN-γ

NK cytotoxic activity and degranulation were examined as a measure of NK cell function. Cytotoxic activity was assessed as previously described (13, 14, 36). Briefly, PBMCs were isolated from whole blood samples following density gradient centrifugation. PBMCs (1×106 cells/ml) were labelled with 0.4% Paul Karl Horan (PKH)-26 fluorescent cell linker dye (Sigma-Aldrich, St Louis, USA). Cells were incubated with K562 tumour cells (1×105 cells/ml) for 4h at three effector to target ratios: 12.5:1, 25:1 and 50:1. A control sample containing no PBMCs was also included as a determinant of cells undergoing spontaneous apoptosis. In advance of flow cytometric analysis, cells were stained with annexin V and 7-aminoactinomycin D (BD Pharmingen, San Diego, CA, USA). The percentage lysis was calculated for each sample at the different ratios as described previously (13, 14, 36). CD107a and IFN-γ production under two stimulatory conditions was used as a measure of NK cell degranulation. In short, cells were stained with CD107a, monensin (BD Biosciences) and Brefeldin A (Golgi Plug; BD Biosciences) with or without K562 cells (1×105/ml) or PMA/I [2.5 μg/ml of PMA and 1 µg/ml of ionomycin (Sigma–Aldrich)] (37, 38). Samples were incubated at 37°C with 5% CO2 for 6h. Intracellular staining was then performed as described above for the detection of IFN-γ.

Th1/Th2/Th17 cytokines following mitogenic stimulation

The levels of Th1/Th2/Th17 cytokines were examined via a cytometric bead array (CBA; BD Pharmingen) following mitogenic stimulation of PBMCs. Briefly, PBMCs isolated from whole blood were placed in cell culture for 72h at a cell concentration of 1×106 cells/ml in RPMI culture media with or without 1 µg of PHA at 37°C with 5% CO2. After 72h, cell supernatants were removed and stored at −80°C for later assessment. Th1, Th2 and Th17 cytokine profiles including IL-2, IL-4, IL-6, IL-10, TNF-α, INF-γ and IL-17A were determined using a CBA kit according to the manufacturer’s instructions (35, 39).

Flow cytometric analysis

All experiments were analysed on a dual laser four colour flow cytometer, BD FACSCalibur (BD Biosciences). The specific combination of mAbs for each assay is presented in Table 1, while the gating strategies for cell phenotypes are presented in Supplementary Figures 1–6, available at International Immunology Online. The quantity for each phenotype was enumerated using the percentage of gated events from flow cytometric analysis and absolute lymphocyte count from haematological counts (32).

Table 1.

Combinations of various antibodies used in examining the different cell attributes

Cell typeAntibody combinations
ImmatureMemoryPlasma cells
B cells CD19-APC CD19-APC CD19-PerCP   
 CD20-PerCP CD20-PerCP CD138-APC   
 CD21-FITC CD21-FITC CD38-FITC   
 CD10-PE CD27-PE CD27-PE   
γδT cells γδ1T cells γδ2T cells    
γδ1-TCR FITC γδ2-TCR-PE CD3-PerCP CD3-PerCP  
CD3-PerCP CD3-PerCP γδTCR-APC γδTCR-APC  
CD45RA-APC CD45RA-APC CD11a-FITC CD94-PE  
CD27-PE CD27-FITC CD62L-PE   
Tregs CD4-FITC CD25-APC CD127-PerCP-CY-5.5 FOXP3-PE  
CD4+T cells CD4-FITC CD39-APC CD73-PE CD127 PerCP-Cy 5.5  
NK cells Phenotypes Degranulation Cytotoxic activity   
    CD3-PerCP     CD3-PerCP     PKH-26   
    CD56-PE     CD56-PE     7-AAD   
    CD16-FITC     CD107a-FITC     Annexin-V-FITC   
     IFN-γ-APC    
Dendritic cells mDC pDC CD80 expression CD86 expression  
    Lin2-FITC     Lin2-FITC     Lin2-FITC     Lin2-FITC  
    HLA-DR-PERCP     HLA-DR-PERCP     HLA-DR-PERCP     HLA-DR-PERCP  
    CD11c-PE     CD123-PE     CD80-PE     CD86-PE  
    CD33-APC     
Neutrophils HNA-1 HNA-2 HNA-3 HNA-4  
    CD16-PE-Cy-5     CD16-PE-Cy-5     CD16-PE-Cy-5     CD16-PE-Cy-5  
    CD62L-APC     CD62L-APC     CD62L-APC     CD62L-APC  
    CD66-FITC     CD66-PE     CD66-FITC     CD66-FITC  
    CD16b-PE     CD177-FITC     CD11b/18-PE     C11a-PE  
KIR-expressing cells KIR2DL1 KIR3DL1 KIR2DL2/L3 KIR2DS4 NKG2D 
    CD158a-APC     CD158e-APC     CD158b-APC     CD158i-APC     NKG2D-APC 
    CD56-PE     CD56-PE     CD56-PE     CD56-PE     CD56-PE 
    CD16-FITC     CD16-FITC     CD16-FITC     CD16-FITC     CD16-FITC 
    CD3-PerCP     CD3-PerCP     CD3-PerCP     CD3-PerCP     CD3-PerCP 
Cell typeAntibody combinations
ImmatureMemoryPlasma cells
B cells CD19-APC CD19-APC CD19-PerCP   
 CD20-PerCP CD20-PerCP CD138-APC   
 CD21-FITC CD21-FITC CD38-FITC   
 CD10-PE CD27-PE CD27-PE   
γδT cells γδ1T cells γδ2T cells    
γδ1-TCR FITC γδ2-TCR-PE CD3-PerCP CD3-PerCP  
CD3-PerCP CD3-PerCP γδTCR-APC γδTCR-APC  
CD45RA-APC CD45RA-APC CD11a-FITC CD94-PE  
CD27-PE CD27-FITC CD62L-PE   
Tregs CD4-FITC CD25-APC CD127-PerCP-CY-5.5 FOXP3-PE  
CD4+T cells CD4-FITC CD39-APC CD73-PE CD127 PerCP-Cy 5.5  
NK cells Phenotypes Degranulation Cytotoxic activity   
    CD3-PerCP     CD3-PerCP     PKH-26   
    CD56-PE     CD56-PE     7-AAD   
    CD16-FITC     CD107a-FITC     Annexin-V-FITC   
     IFN-γ-APC    
Dendritic cells mDC pDC CD80 expression CD86 expression  
    Lin2-FITC     Lin2-FITC     Lin2-FITC     Lin2-FITC  
    HLA-DR-PERCP     HLA-DR-PERCP     HLA-DR-PERCP     HLA-DR-PERCP  
    CD11c-PE     CD123-PE     CD80-PE     CD86-PE  
    CD33-APC     
Neutrophils HNA-1 HNA-2 HNA-3 HNA-4  
    CD16-PE-Cy-5     CD16-PE-Cy-5     CD16-PE-Cy-5     CD16-PE-Cy-5  
    CD62L-APC     CD62L-APC     CD62L-APC     CD62L-APC  
    CD66-FITC     CD66-PE     CD66-FITC     CD66-FITC  
    CD16b-PE     CD177-FITC     CD11b/18-PE     C11a-PE  
KIR-expressing cells KIR2DL1 KIR3DL1 KIR2DL2/L3 KIR2DS4 NKG2D 
    CD158a-APC     CD158e-APC     CD158b-APC     CD158i-APC     NKG2D-APC 
    CD56-PE     CD56-PE     CD56-PE     CD56-PE     CD56-PE 
    CD16-FITC     CD16-FITC     CD16-FITC     CD16-FITC     CD16-FITC 
    CD3-PerCP     CD3-PerCP     CD3-PerCP     CD3-PerCP     CD3-PerCP 

The table shows a representation of all the antibodies used in identifying cell phenotypes, receptors and function. AAD, aminoactinomycin D; APC, allophycocyanin; FITC, fluorescein isothiocyanate; KIR, killer cell immunoglobulin-like receptor; PE, phycoerythrin; PerCP, peridinin chlorophyll protein complex.

Table 1.

Combinations of various antibodies used in examining the different cell attributes

Cell typeAntibody combinations
ImmatureMemoryPlasma cells
B cells CD19-APC CD19-APC CD19-PerCP   
 CD20-PerCP CD20-PerCP CD138-APC   
 CD21-FITC CD21-FITC CD38-FITC   
 CD10-PE CD27-PE CD27-PE   
γδT cells γδ1T cells γδ2T cells    
γδ1-TCR FITC γδ2-TCR-PE CD3-PerCP CD3-PerCP  
CD3-PerCP CD3-PerCP γδTCR-APC γδTCR-APC  
CD45RA-APC CD45RA-APC CD11a-FITC CD94-PE  
CD27-PE CD27-FITC CD62L-PE   
Tregs CD4-FITC CD25-APC CD127-PerCP-CY-5.5 FOXP3-PE  
CD4+T cells CD4-FITC CD39-APC CD73-PE CD127 PerCP-Cy 5.5  
NK cells Phenotypes Degranulation Cytotoxic activity   
    CD3-PerCP     CD3-PerCP     PKH-26   
    CD56-PE     CD56-PE     7-AAD   
    CD16-FITC     CD107a-FITC     Annexin-V-FITC   
     IFN-γ-APC    
Dendritic cells mDC pDC CD80 expression CD86 expression  
    Lin2-FITC     Lin2-FITC     Lin2-FITC     Lin2-FITC  
    HLA-DR-PERCP     HLA-DR-PERCP     HLA-DR-PERCP     HLA-DR-PERCP  
    CD11c-PE     CD123-PE     CD80-PE     CD86-PE  
    CD33-APC     
Neutrophils HNA-1 HNA-2 HNA-3 HNA-4  
    CD16-PE-Cy-5     CD16-PE-Cy-5     CD16-PE-Cy-5     CD16-PE-Cy-5  
    CD62L-APC     CD62L-APC     CD62L-APC     CD62L-APC  
    CD66-FITC     CD66-PE     CD66-FITC     CD66-FITC  
    CD16b-PE     CD177-FITC     CD11b/18-PE     C11a-PE  
KIR-expressing cells KIR2DL1 KIR3DL1 KIR2DL2/L3 KIR2DS4 NKG2D 
    CD158a-APC     CD158e-APC     CD158b-APC     CD158i-APC     NKG2D-APC 
    CD56-PE     CD56-PE     CD56-PE     CD56-PE     CD56-PE 
    CD16-FITC     CD16-FITC     CD16-FITC     CD16-FITC     CD16-FITC 
    CD3-PerCP     CD3-PerCP     CD3-PerCP     CD3-PerCP     CD3-PerCP 
Cell typeAntibody combinations
ImmatureMemoryPlasma cells
B cells CD19-APC CD19-APC CD19-PerCP   
 CD20-PerCP CD20-PerCP CD138-APC   
 CD21-FITC CD21-FITC CD38-FITC   
 CD10-PE CD27-PE CD27-PE   
γδT cells γδ1T cells γδ2T cells    
γδ1-TCR FITC γδ2-TCR-PE CD3-PerCP CD3-PerCP  
CD3-PerCP CD3-PerCP γδTCR-APC γδTCR-APC  
CD45RA-APC CD45RA-APC CD11a-FITC CD94-PE  
CD27-PE CD27-FITC CD62L-PE   
Tregs CD4-FITC CD25-APC CD127-PerCP-CY-5.5 FOXP3-PE  
CD4+T cells CD4-FITC CD39-APC CD73-PE CD127 PerCP-Cy 5.5  
NK cells Phenotypes Degranulation Cytotoxic activity   
    CD3-PerCP     CD3-PerCP     PKH-26   
    CD56-PE     CD56-PE     7-AAD   
    CD16-FITC     CD107a-FITC     Annexin-V-FITC   
     IFN-γ-APC    
Dendritic cells mDC pDC CD80 expression CD86 expression  
    Lin2-FITC     Lin2-FITC     Lin2-FITC     Lin2-FITC  
    HLA-DR-PERCP     HLA-DR-PERCP     HLA-DR-PERCP     HLA-DR-PERCP  
    CD11c-PE     CD123-PE     CD80-PE     CD86-PE  
    CD33-APC     
Neutrophils HNA-1 HNA-2 HNA-3 HNA-4  
    CD16-PE-Cy-5     CD16-PE-Cy-5     CD16-PE-Cy-5     CD16-PE-Cy-5  
    CD62L-APC     CD62L-APC     CD62L-APC     CD62L-APC  
    CD66-FITC     CD66-PE     CD66-FITC     CD66-FITC  
    CD16b-PE     CD177-FITC     CD11b/18-PE     C11a-PE  
KIR-expressing cells KIR2DL1 KIR3DL1 KIR2DL2/L3 KIR2DS4 NKG2D 
    CD158a-APC     CD158e-APC     CD158b-APC     CD158i-APC     NKG2D-APC 
    CD56-PE     CD56-PE     CD56-PE     CD56-PE     CD56-PE 
    CD16-FITC     CD16-FITC     CD16-FITC     CD16-FITC     CD16-FITC 
    CD3-PerCP     CD3-PerCP     CD3-PerCP     CD3-PerCP     CD3-PerCP 

The table shows a representation of all the antibodies used in identifying cell phenotypes, receptors and function. AAD, aminoactinomycin D; APC, allophycocyanin; FITC, fluorescein isothiocyanate; KIR, killer cell immunoglobulin-like receptor; PE, phycoerythrin; PerCP, peridinin chlorophyll protein complex.

Statistical analysis

Statistical analysis was performed using SPSS software version 16.0 (SPSS Inc., Chicago, IL, USA) and Graph Pad Prism software version 6.0 (Graph Pad Software, Inc., San Diego, CA, USA). Pairwise comparison using multivariate testing was used to perform comparative assessments of all data generated from all participants (CFS/ME and non-fatigued control subjects). The analysis of variance test and independent sample t-test were used to determine the significance of changes in the variables measured. The Bonferroni method was used as post-hoc analysis to assess changes in the data. A test for normality was performed using Kolmogorov–Smirnov and Shapiro–Wilk normality tests. Spearman’s rank correlation was the non-parametric test used to examine correlations in the data generated. Data are represented as mean ± standard error of the mean. All significant results had P values ≤ 0.05.

Results

Patient blood characteristics

Prior to all flow cytometric testing of immune cell phenotypes, all participants were assessed on blood characteristics (Table 2). There were significant differences between CFS/ME patients and the non-fatigued controls on measures of blood parameters including platelets, haematocrit and erythrocyte sedimentation rate. All other parameters remained unchanged. All CFS/ME patients met the CDC 1994 criteria for CFS/ME. The duration of CFS/ME among the patients was over 2 years.

Table 2.

Comparative assessment of blood characteristics in CFS/ME patients and non-fatigued controls

Blood MarkersCFS/ME Patients (n=30)Controls (n=25)P values
Haemoglobin (g/l) 134.43±2.41 140.67±2.56 0.08 
White Cell Count (×109/l) 6.41±0.33 6.06±0.31 0.51 
Platelets (×109/l) 274.61±13.11 238.38±9.39 0.05 
Haematocrit 0.41±0.006 0.43±0.007 0.05 
Red Cell Count (×1012/l) 4.46±0.08 4.65±0.09 0.12 
Mean Cell volume (fl) 91.86±0.57 92.08±0.51 0.65 
Neutrophils (×109/l) 3.90±0.27 3.84±0.26 0.95 
Lymphocytes (109/l) 1.92±0.10 1.73±0.09 0.20 
Monocytes (×109/l) 0.33±0.018 0.32±0.02 0.64 
Eosinophils (×109/l) 0.16±0.019 0.15±0.02 0.69 
Basophils (×109/l) 0.03±0.004 0.03±0.004 0.53 
Erythrocyte sedimentation rate (mm/Hr) 17.22±2051 10.48±1.70 0.05 
Blood MarkersCFS/ME Patients (n=30)Controls (n=25)P values
Haemoglobin (g/l) 134.43±2.41 140.67±2.56 0.08 
White Cell Count (×109/l) 6.41±0.33 6.06±0.31 0.51 
Platelets (×109/l) 274.61±13.11 238.38±9.39 0.05 
Haematocrit 0.41±0.006 0.43±0.007 0.05 
Red Cell Count (×1012/l) 4.46±0.08 4.65±0.09 0.12 
Mean Cell volume (fl) 91.86±0.57 92.08±0.51 0.65 
Neutrophils (×109/l) 3.90±0.27 3.84±0.26 0.95 
Lymphocytes (109/l) 1.92±0.10 1.73±0.09 0.20 
Monocytes (×109/l) 0.33±0.018 0.32±0.02 0.64 
Eosinophils (×109/l) 0.16±0.019 0.15±0.02 0.69 
Basophils (×109/l) 0.03±0.004 0.03±0.004 0.53 
Erythrocyte sedimentation rate (mm/Hr) 17.22±2051 10.48±1.70 0.05 

The table shows representative data of all the blood characteristics obtained from full blood count analysis. The units of measurement are specified in parentheses. Data are represented as means ± standard error of the mean. P < 0.05 are in bold.

Table 2.

Comparative assessment of blood characteristics in CFS/ME patients and non-fatigued controls

Blood MarkersCFS/ME Patients (n=30)Controls (n=25)P values
Haemoglobin (g/l) 134.43±2.41 140.67±2.56 0.08 
White Cell Count (×109/l) 6.41±0.33 6.06±0.31 0.51 
Platelets (×109/l) 274.61±13.11 238.38±9.39 0.05 
Haematocrit 0.41±0.006 0.43±0.007 0.05 
Red Cell Count (×1012/l) 4.46±0.08 4.65±0.09 0.12 
Mean Cell volume (fl) 91.86±0.57 92.08±0.51 0.65 
Neutrophils (×109/l) 3.90±0.27 3.84±0.26 0.95 
Lymphocytes (109/l) 1.92±0.10 1.73±0.09 0.20 
Monocytes (×109/l) 0.33±0.018 0.32±0.02 0.64 
Eosinophils (×109/l) 0.16±0.019 0.15±0.02 0.69 
Basophils (×109/l) 0.03±0.004 0.03±0.004 0.53 
Erythrocyte sedimentation rate (mm/Hr) 17.22±2051 10.48±1.70 0.05 
Blood MarkersCFS/ME Patients (n=30)Controls (n=25)P values
Haemoglobin (g/l) 134.43±2.41 140.67±2.56 0.08 
White Cell Count (×109/l) 6.41±0.33 6.06±0.31 0.51 
Platelets (×109/l) 274.61±13.11 238.38±9.39 0.05 
Haematocrit 0.41±0.006 0.43±0.007 0.05 
Red Cell Count (×1012/l) 4.46±0.08 4.65±0.09 0.12 
Mean Cell volume (fl) 91.86±0.57 92.08±0.51 0.65 
Neutrophils (×109/l) 3.90±0.27 3.84±0.26 0.95 
Lymphocytes (109/l) 1.92±0.10 1.73±0.09 0.20 
Monocytes (×109/l) 0.33±0.018 0.32±0.02 0.64 
Eosinophils (×109/l) 0.16±0.019 0.15±0.02 0.69 
Basophils (×109/l) 0.03±0.004 0.03±0.004 0.53 
Erythrocyte sedimentation rate (mm/Hr) 17.22±2051 10.48±1.70 0.05 

The table shows representative data of all the blood characteristics obtained from full blood count analysis. The units of measurement are specified in parentheses. Data are represented as means ± standard error of the mean. P < 0.05 are in bold.

Alterations in Tregs, B cells and DC subsets in CFS/ME

The total number of lymphocytes, monocytes or neutrophils did not differ between the two population groups. Similarly, routine blood assessments demonstrated similarities in all blood characteristics measured (Table 2). A significant difference in cell subsets was observed in DCs, B-cell phenotypes and Tregs [Fig. 1(a–c)]. pDCs were significantly decreased in the CFS/ME group, while the mDCs although elevated in the CFS/ME group did not achieve statistical significance (Fig. 1a). CFS/ME patients showed a significant decrease in immature B cells and an increase in memory B cells in comparison with the non-fatigued control population (Fig. 1b). Tregs and CD4+CD73+CD39 T cells were elevated in CFS/ME group in comparison with the non-fatigued controls, while no difference was observed in CD4+CD73CD39+cells in both participant groups (Fig. 1c).

Fig. 1.

Examination of DCs, B cells and Treg phenotypes in CFS/ME. Phenotypes of blood cells were determined following staining with mAbs. This allowed the enumeration of DCs subsets (1a), B cells (1b) and Tregs (1c) in patients with CFS/ME in comparison to a non-fatigued group. Data are represented as mean ± standard error of the mean (SEM). *P < 0.05.

Decreases in antigens with no significant changes in surface receptors in CFS/ME

HNA-2 (CD177+) was significantly reduced on the surface of neutrophils from CFS/ME patients in comparison with the non-fatigued controls (Fig. 2). When we examined the expression of co-stimulatory markers, CD80 and CD86 pre- and post-stimulation, we failed to observe any significant changes in the expression of these markers on the surfaces of DCs. Equally, there were no significant differences in the expression of killer cell immunoglobulin-like receptors (KIRs) or NKG2D on the surfaces of isolated NK cells between the two groups of participants. Similarly, there were no significant differences observed in the following cell surface molecules on γδT cells: CD62Lnegative, CD62Llow, CD62Lhigh, CD11alow, CD11ahigh, CD94+ and CD94.

Fig. 2.

Expression of HNA antigens in CFS/ME. Neutrophil antigens were examined using mAbs for their specific markers. Data are represented as mean ± SEM. *P < 0.05.

Reduced NK cell function in CFS/ME

Reduced cytotoxic activity may be a prominent hallmark of CFS/ME as it has consistently been reported in CFS/ME patients. In this study, we confirmed significant reductions in the ability of NK cells from CFS/ME patients to lyse K562 cells, at 25:1 and 50:1 effector:target ratios (Fig. 3a). Conversely, degranulation was increased in CFS/ME patients in PMA/I and K562 samples (Fig. 3b). Similarly, IFN-γ-producing CD3CD56+NK cells were increased in the CFS/ME patients compared with the non-fatigued controls (Fig. 3d). As cytotoxic activity is routinely decreased in CFS/ME patients, we assessed the intracellular levels of these lytic proteins (perforin, GZA and GZB) in NK and T cells. There were no significant differences in the levels of perforin or GZA in the NK cells of both CFS/ME patients and the non-fatigued controls. However, GZB was significantly reduced, while IFN-γ levels in CD3CD56+NK cells were increased in the CFS/ME patients (Fig. 3c).

Fig. 3.

NK cell cytotoxic activity, degranulation and proteins in CFS/ME. NK function was examine in relation to cytotoxic activity in the presence of tumour cell lines (3a), degranulation using K562 and PMA/I (3b) and intracellular levels of proteins, perforin, GZA, GZB and IFN-γ (3c and 3d) in the CFS/ME group in comparison to the non-fatigued controls. Data are represented as mean ± SEM. *P < 0.05.

T-cell proteins in CFS/ME

Cytokines are released following inflammatory or mitogenic stimulation. In this study, the secretion of Th1/Th2/Th17 specific cytokines was measured following stimulation with PHA. In the CFS/ME patient group, IFN-γ was significantly decreased, while TNF-α increased significantly following stimulation with PHA (Fig. 4).

Fig. 4.

Cytokine levels following stimulation with PHA in CFS/ME. Cytokine secretion was assessed using the CBA kit. The concentrations of these cytokines are expressed as pg/ml. Data are represented as mean ± SEM. *P < 0.05.

Correlation studies

A Spearman’s rank correlation coefficient was calculated to assess the relationship between the parameters examined. A positive correlation was observed between the following variables: Tregs and CD73+T cells (r = 0.444, P = 0.014), HNA-2 and immature B cells (r = 0.429, P = 0.018), degranulation and IFN-γ (r = 0.845, P = 0.0001) and HNA-2 and NK cytotoxic activity (r = 0.890, P = 0.001). While negative correlations were observed between the following parameters: degranulation and NK cytotoxic activity (r = −0.368, P = 0.045, r = −0.556, P = 0.001), Tregs and cytotoxic activity (r = −0.405, P = 0.027), HNA-2 (CD177+) and HNA-2 (CD177; r = −0.418, P = 0.021) and lastly between HNA-2 and degranulation (r = −0.412, P = 0.024).

Discussion

CFS/ME is viewed as a multifactorial and heterogeneous disorder with varying immunological abnormalities. This is the first study to examine the role of immune cell phenotypes particularly γδT cells, DCs and Treg subsets and neutrophil antigens in CFS/ME patients. This is also the first study to report on NK cell degranulation in CFS/ME. The results from this study suggest that the mechanism for CFS/ME may involve decreases in NK cell activity, NK lytic proteins, pDCs, IFN-γ, HNA-2 antigens and memory B cells, concurrent with increases in immature B cells, NK cell degranulation, TNF-α and NK cell specific IFN-γ.

Neutrophil antigens have clinical significance in both alloimmune and autoimmune diseases. Presently, five neutrophil antigens have been classified including HNA-1, HNA-2, HNA-4, HNA-5 and HNA-3, and four of these antigens have known carrier glycoproteins CD16b, CD177, CD11b and CD11a, respectively (40). The glycoprotein specific to HNA-3 is currently unknown. HNA-2 is the most polymorphic neutrophil antigen and is not expressed on all neutrophils. It is elevated in patients with bacterial infections (41); however, this did not occur in this study. Altered expression of CD177 has been reported in a number of autoimmune disorders (42–45). CD177 is an integral molecule in neutrophil migration across the endothelial surface as it binds to the platelet endothelial cell adhesion molecule-1 (PECAM-1) on the endothelial cells (46). Increased transmigration of neutrophils from the blood to sites of infection may possibly cause low levels of CD177-expressing neutrophils in the periphery of patients with CFS/ME. This may also indicate potential abnormalities in the migratory properties of these neutrophils. As respiratory burst has been reported to be decreased in neutrophils from CFS/ME patients (13), an in-depth examination of other neutrophil attributes may articulate their profile in CFS/ME. The cause of the increases in platelets and erythrocyte sedimentation rate in the CFS/ME patients is not entirely understood as these have not been consistently shown in CFS/ME cases.

Similar to previous studies, this study confirmed decreases in NK cytotoxic activity among the CFS/ME patients. In CFS/ME, reduced NK cytotoxic activity may be associated with differential expression in micro-RNA, messenger RNA and protein levels of lytic proteins (12, 14). Lytic proteins, perforin and granzymes are important facilitators of granule dependent cytotoxic pathways. Upon release of granzymes into the infected cells, these proteins activate caspase independent and dependent pathways that ensure apoptosis of virally infected or tumour cells (47). Excessive amounts of GZB are advantageous as it incapacitates viral inhibitory signals and prevents the persistence of viral pathogens (48). Deficiencies in GZB may promote deferments in processes such as rapid nuclear disintegration or DNA fragmentation of virally infected cells allowing the prevalence of certain viruses (49). Importantly, GZB is the most prominent constituent of the NK granules (50) thus, a reduction in GZB within the NK cell may potentially contribute to the reduced cytotoxicity of NK cells in CFS/ME patients. Synergistic activities between perforin and GZB are necessary for the induction of apoptosis. Reduced NK cytotoxic activity concomitant with increases in NK degranulation denotes substantial breakdown in NK cytotoxic activity. Usually, NK cell degranulation is directly correlated with NK cell cytotoxic activity (51); in this study, reduced NK cytotoxic activity was inversely correlated with an increased NK cell degranulation and IFN-γ. Thus, the NK cells in the CFS/ME patients are presumably highly activated in response to a potential viral overload but incapable of eliminating these viruses. Additionally, an overabundance of Tregs is an added hindrance to NK cytotoxic activity in the CFS/ME patients.

Although reduced pDCs were inversely correlated with reduced cytotoxic activity, these may also contribute to the NK cytotoxic profile in CFS/ME patients as pDCs are important producers of type I interferons, which enhance NK cytotoxic activity (52). Thus, reduced pDCs may result in reduced levels of type I interferons, impeding effective elimination of pathogens in CFS/ME patients (53). Type I interferons include IFN-α and IFN-β. In vitro studies have shown a protective mechanism of these molecules on memory B cells, where they have been shown to maintain and promote the longevity of memory B cells by inhibiting cell death (54). IFN-α is increased in some CFS/ME patients (55) and this potentially enhances the survival of memory B cells.

The pattern of B-cell distribution observed in this study has been reported in autoimmune diseases such as multiple sclerosis (MS) and rheumatoid arthritis (RA). In RA patients, naive B cells are decreased and this may coincide with an increase in memory B cells (56). Similarly, patients with activated systemic lupus erythematosus (SLE) demonstrate a comparable trend in B cells (57), while patients with MS have increased levels in memory B cells (58). Disturbed B-cell homeostasis has been observed in CFS/ME patients (59). High levels of memory B cells may reflect defects in the priming mechanisms required for B-cell differentiation into plasma cells or migration of these cells from other tissues such as the spleen (60). Memory B cells are heterogeneous and can therefore be further subdivided in to phenotypes including IgD+ and IgD, unswitched and switched memory cells, respectively (61). Alterations in these phenotypes have been associated with a number of autoimmune diseases, for example in Sjogren’s disease, IgM+ memory B cells are increased, while increases in double negative IgDCD27 switched memory cells have been observed in SLE (62, 63).

The Treg profile in this study was similar to previous findings (14, 64). The cause of heightened levels of Tregs in CFS/ME is unknown. Surface expression of CD73 on CD4+ T cells was correlated with an increase in Tregs in the CFS/ME patients. CD73 is an ectonucleotidase required for adenosine production (65). At optimal conditions, CD4+ T cells, in particular Tregs produce low levels of adenosine deaminases (ADA), which breaks down adenosine (66). High levels of CD73 with limited amounts of ADA enhance the levels of extracellular adenosine in circulation and this may accumulate in immune cells altering their functions (67). On the cell surface of many leucocytes including Tregs, ADA binds to CD26 eliminating anti-inflammatory adenosine (66) and reducing suppression by Tregs. Reduced levels of CD26 have been reported on lymphocytes in CFS/ME patients and this in conjunction with an increase in CD73 may increase Treg suppression in CFS/ME (68). Tregs may suppress NK cytotoxic activity and T helper cells via a number of pathways including the generation of adenosine by CD39 and CD73 molecules (69, 70). In the presence of adenosine, NK cells also produce high levels of IFN-γ (71). Extracellular adenosine has anti-inflammatory effects on both innate and adaptive immunes, specifically, it may dampen the activation of Th1 and Th17 cells (72, 73). Adenosine may also inhibit CD8+ T cell cytotoxic activity and the release of pro-inflammatory cytokines by CD4+ T cell subsets (74–76). In mice, the presence of adenosine receptor agonist reduces NK cytotoxic activity due to a decrease in cyclic adenosine monophosphate (77). In T cells, CD73 dampens the release of pro-inflammatory cytokines by inhibiting NFκB activation (78), promoting a Th2 type response. High levels of adenosine or ATP activates a negative feedback process that inhibits neutrophil function and protects against prolonged inflammation or injury (79, 80).

In summary, the findings from this study confirm a substantial breakdown in immune tolerance and inflammatory mechanisms in patients with CFS/ME. This likely involves significant impairments in the NK-cell function, over-reactive Tregs, impaired DCs, neutrophils, dysregulation in cytokine levels and abnormal production of adenosine. Collectively, these defects are overwhelming and further confirmatory studies may be required owing to the multifactorial and heterogeneous nature of the disorder. Importantly, it may be necessary to confirm the levels of circulating type I interferons in CFS/ME patients and the exact profile of memory B cells and immature B cells that are disproportional in CFS/ME.

Supplementary data

Supplementary data are available at International Immunology Online.

Funding

The Alison Hunter Memorial Foundation (43120); the Mason Foundation grant (HF201); the Smartstate Future Funds.

Acknowledgement

The authors would like to thank all the participants for their contribution to this study.

Conflict of interest: The authors have no conflict of interest.

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