Proteomics and cytokine analyses distinguish myalgic encephalomyelitis/chronic fatigue syndrome cases from controls

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a serious disease that can be diagnosed following 6 months of new debilitating fatigue, post-exertional malaise, unrefreshing sleep, and either or both of two additional symptoms, cognitive difficulty or orthostatic intolerance [1]. Most patients report that their symptoms arose after a viral-like illness, but the identity of the preceding infection is almost always unknown, although the enteroviral family has sometimes been implicated [2, 3]. Before 2020, 65 million individuals world-wide were estimated to experience ME/CFS [4]. Since the SARS-COV2 pandemic, a subset of individuals who had suffered acute COVID-19 have been continuing to experience symptoms [5], and some victims of Long COVID fulfill the ME/CFS diagnostic criteria described above [6]. Likewise, individuals experiencing Gulf War Illness have symptom overlap with both Long COVID and ME/CFS [7]. However, a number of assays, such as neuroimaging [8], distinguish Gulf War Illness and ME/CFS. Whether Long COVID and ME/CFS not associated with SARS-CoV-2 infection will likewise be differentiated through imaging or other measures is not yet known.

Proteins related to the innate immune system and involved in the complement cascade as well as in pathways related to dopamine signaling have been reported to be enriched in ME/CFS patients compared to controls in studies analyzing cerebrospinal fluid [9, 10]. Through plasma mass spectrometry analysis, dysregulations in energy, lipid and amino acid metabolism were also reported in ME/CFS [11‐13]. But more recently, a ME/CFS-related plasma proteome analysis using untargeted ultra-performance liquid chromatography-tandem mass spectrometry identified differing profiles between ME/CFS patients, as well as ME/CFS subgroups (with or without IBS), and controls and a set of proteins that may predict ME/CFS status with a reasonably high degree of accuracy (Area Under the Curve (AUC) = 0.774–0.838) [14].

It is known that immune function and inflammatory responses are regulated by cytokines acting as modulators, and that their secretion can occur in classical secretion manner or via encapsulation in extracellular vesicles, protecting them from degrading enzymes [15]. EVs are one of the main participants in cell-to-cell communication and drive inflammatory, autoimmune and infectious disease pathology [16‐19] and previous reports have shown increased numbers of circulating EVs, not only in cancers and Alzheimer’s disease [17, 20‐22], but also in ME/CFS [23‐25]. A recent study on EVs isolated from ME/CFS patients and from subjects with idiopathic chronic fatigue and clinical depression was able to distinguish the two groups with an AUC of 0.802 solely using circulating EV numbers, which allowed a correct diagnosis in 90–94% of ME/CFS cases [24].

Further molecular characterization of ME/CFS is urgently needed to provide insights into the disruptions that occur in the illness. Multi-omic studies performed on the same set of subjects have high potential to provide new hypotheses. Furthermore, being able to distinguish ME/CFS subjects from healthy controls at high sensitivity and specificity would allow monitoring of the effect of experimental therapies. Utilization of blood samples to assess ME/CFS-associated abnormalities would be particularly valuable in comparison to methods that are more invasive or cumbersome.

In this study, we isolated extracellular vesicles (EVs) from blood samples collected prior to 2020 from ME/CFS subjects and heathy controls and measured their cytokine content. This newly generated data along with data already published from a tandem mass-spectrometry plasma proteomic analysis [14] and plasma cytokine levels determination [26] on the same samples were used all together for multiple statistical analysis. We identified a suite of EV cytokines that significantly differ in levels between ME/CFS subjects and controls. We observed correlations between levels of different EV cytokines, between levels of plasma cytokines, between EV cytokines and plasma cytokines, and between cytokines and other plasma proteins. We also detected relationships between plasma cytokines and severity of certain ME/CFS symptoms. In controls, levels of four plasma proteins were related to health measures. A protein involved in hemostasis, SERPINA5, was positively correlated with higher SF-36 function scores. Using machine learning, we identified the 20 proteins with the highest feature importance values. Using these 20 analytes and XGBoost, we could discriminate ME/CFS and controls subjects at an extremely high sensitivity and specificity (AUC = 0.947).

In this study, we utilized samples and data from 98 of the 100 subjects who previously provided samples that were analyzed for fecal metagenomics and plasma cytokines [26] and also for plasma proteins assayed by mass spectrometry [14]. Furthermore, extracellular vesicles were isolated from these 98 samples, and we found that the mean size and concentrations of particles were significantly higher in ME/CFS (Fig. 2). Although a previous report using the same EV purification method as the present study found that the mean size of ME/CFS EVs was reduced [42], the authors analyzed EVs isolated from 10 ME/CFS patients and 5 healthy controls vs. 49 ME/CFS and 49 controls in this study, did not use thrombin to remove fibrinogen and used low centrifugal forces to pellet EVs (1500 g vs. 12,000 g in this study). All together this could explain the different results observed with our current study. Finally, our results confirmed other findings reporting higher concentration of vesicles in ME/CFS [24, 25, 42] and these observations are also seen in conditions such as Alzheimer’s disease [17] and cerebrovascular disease [20].

Our work demonstrates the value of using multiple assays on the same samples, and also the importance of performing correlations with clinical data. Doing so has allowed us to identify a number of associations of particular proteins with patient symptoms. Importantly, we demonstrate that the data can distinguish between patients and controls at high accuracy. ME/CFS has long been incorrectly viewed by some as a psychological illness. Being able to separate patients and controls through analyses of plasma is a strong demonstration of the biological natures of the illness. A summary of our experimental assays and key findings is shown in Fig. 9.

Our current analysis of 98 samples agrees with the prior comparison of plasma cytokines in 100 ME/CFS and controls, which did not identify any significant differences between cohorts after adjustment for multiple testing [26]. In contrast, we identified 17 EV cytokines that distinguish patients and controls with adjusted p-values of less than 0.2, all higher in ME/CFS subjects. Out of these 17 proteins, the majority (10 out of 17) are known to be pro-inflammatory cytokines/chemokines (TNFα, IL1β, CXCL8, CXCL1, IL15, CCL7, IL17, CCL5, IL1α and IL1R1), 5 are related to adaptive immunity (IL2, CSF2, IL3, IL4 and IL7), IL12p40 has anti-inflammatory properties and NGFβ is both pro- and anti-inflammatory. Higher levels of pro-inflammatory cytokines are in line with previous reports [43‐45].

Although differences in EV cytokine levels did not reach statistical significance after correction for multiple comparison in a prior pilot study with only 38 subjects, 13 of the 17 EV cytokines in the present study were also found at higher levels in EVs from ME/CFS subjects in comparison to controls [25]. The most significant difference was IL2 (q = 0.007, Fig. 3). IL2 is a secreted cytokine produced by activated CD4 + and CD8 + T lymphocytes and promotes strong proliferation of activated B-cells and subsequently immunoglobulin production. It plays a pivotal role in regulating the adaptive immune system by controlling the survival and proliferation of regulatory T-cells. IL2 levels were found to be higher in cerebrospinal fluid [46] and plasma from ME/CFS patients [47]. The higher levels of IL2 found in EVs in the present study might be part of a specific immune response in ME/CFS. A number of cytokines/chemokines which were observed to be dysregulated are either produced by B cells or are also B cell regulators (e.g. CXCL1 and CXCL12).

Correlations of cytokines with other cytokines provide information about the networks of interactions between signaling molecules. Several other studies demonstrated that the networks of plasma or extracellular vesicle cytokines differ between ME/CFS subjects and controls [25, 41, 44, 48]. We have chosen to display correlations between the three types of data: plasma and EV cytokines and plasma proteomics—using correlograms. Inspection of the visual representation of these protein–protein interactions immediately reveals that there are positive correlations between EV cytokines and between plasma cytokines that occur in cases but not controls and vice versa (Fig. 5). A particularly striking observation is a greater number of positive correlations between plasma cytokines in ME/CFS than in controls (Fig. 5b), indicating that cytokine signaling is substantially different, perhaps reflective of an inflammatory environment.

Seventy-one proteins characterized by mass spectrometry exhibited significant correlations with other plasma proteins (Fig. 5c). For example, F2 exhibited 31 positive correlations with other proteins, of which eleven were seen in cases but not controls. F2 is coagulation factor II or thrombin, and converts fibrinogen to fibrin and activates factors V, VII, VIII, XIII. Thrombin promotes platelet activation and aggregation, but it is also thought to have other functions during inflammation and wound healing [49].

Despite not observing significant differences in levels of plasma cytokines between the two cohorts, we did observe correlations of plasma cytokines with clinical data. CSF2, also known as Granulocyte Monocyte Colony Stimulation factor (GM-CSF), is lower in males in both ME/CFS and controls and increases with BMI in both cohorts according to both the robust linear regression and correlation analyses (Fig. 6a, Tables 2 and 3). In the ME/CFS cohort, with increasing CSF2, scores on the SF36 Physical Function and the MFI-fatigue scales indicate greater impact of physical and fatigue symptoms, respectively. Increase in GM-CSF is associated with chronic inflammation [50]. GM-CSF induces classical monocytes to differentiate into monocyte-derived dendritic cells and macrophages in vitro [51]. Classical monocytes exhibit a unique gene expression pattern in ME/CFS compared to controls [52], and elevated GM-CSF could be a signaling factor involved in this response.

Increases in levels of three cytokines, CCL2, CXCL10, and CCL11 were associated with increasing age only in the ME/CFS cohort, according to Spearman correlations. CCL2, also known as MCP-1 (Monocyte Chemoattractant Protein-1), attracts monocytes across the endothelium into tissues [53], and could also be a factor in the altered monocyte gene expression profile [54]. CCL2 was also observed to decrease with age in the total cohort by both robust linear regression and Spearman correlation, but increases in the ME/CFS cohort with increasing age, according to Spearman correlation (Fig. 6d, Table 3). Using robust linear regression, plasma CCL11 was not significantly increasing with age but EV CCL11 was predicted to be higher with increasing age. CXCL10 (IP10) is also involved in cell migration, in particular, attraction of macrophages, monocytes and activated T and NK cells [55]. CCL11, also known as eotaxin, is known to increase with aging and higher levels are associated with decreased neurogenesis [56]. Two large studies previously observed an association of leptin, GM-CSF, IP10, and eotaxin with ME/CFS severity [43] or higher eotaxin in long-term ME/CFS cases [41]. Almost all of the ME/CFS subjects in this study have been ill more than 3 years.

Higher leptin is correlated with female sex and higher BMI in both patients and controls both by robust linear regression and correlation analyses (Tables 2 and 3). Higher leptin is also associated with IBS in the patient cohort (Fig. 6b). Increase in leptin is also correlated with worse scores on the SF36 physical function measures and MFI-fatigue scale (Fig. 6c). Leptin was previously correlated with fatigue and severity in ME/CFS [43, 54]. Increasing levels of another inflammatory cytokine, TNFα, also correlates with lower patient Physical Function scores on the SF36 and has previously been reported to be elevated in ME/CFS [41, 57, 58] (Fig. 6e).

Higher levels of IL1-RA, which antagonizes IL1 inflammatory cytokines, were associated with higher BMI and lower SF-36 Physical Function in ME/CFS cases (Fig. 6e). Although IL1-RA could be considered to be anti-inflammatory, it is known that IL1-RA levels are higher in obesity [59] and higher levels are considered to be a marker for metabolic dysregulation [60], which could be resulting in the lower physical ability.

We observed that lower levels of the anti-inflammatory cytokine IL13 were associated with lower activity in the ME/CFS cases. IL-13 was previously reported to be lower in females with ME/CFS vs. controls [61]. In contrast, higher IL13 was correlated with increased symptom severity in one study [43], while no difference between cases and controls was seen in another [41].

Higher levels of another protein associated with hemostasis, PROS1, is correlated with poorer health in the controls but has no significant association with the ME/CFS cohort (Fig. 7a). PROS1, also known as Protein S, is a well-known regulator of hemostasis, with important anti-coagulant effects [62]. The fact that it has no correlation with health of ME/CFS patients may reflect disturbed control of hemostasis in the disease.

Higher levels of CETP, Cholesteryl Ester Transfer Protein, are associated with increased fatigue on the MFI-20 (Fig. 7a). This protein controls the exchange of cholesteryl esters and triglycerides between HDL and low-density lipoproteins (LDL), and higher CETP would be expected to result in a less favorable LDL/HDL ratio, which is associated with heart disease [63]. Immune cells in ME/CFS patients have been observed to exhibit altered fatty acid oxidation, which could be related to differences in plasma fatty acid composition [64].

Higher levels of SERPINA5 were associated with better scores on the SF-36 general health and social functioning scales (Fig. 7b). SERPINA5 is a secreted serine protease inhibitor whose functions are not completely understood [65]. It was originally identified as an inhibitor of the anticoagulant protease-activated protein C [66]. While this fact suggests that higher SERPINA5 might increase coagulation, an in vitro study demonstrated that SERPINA5 can serve as both an anti-coagulant and a pro-coagulant depending on the presence of thrombomodulin [67]. Platelets contain SERPINA5 mRNA and can also take up the protein from the external milieu [68]. Our finding of a correlation of ME/CFS health status with a protein involved in hemostasis may be relevant to the recent findings of activated platelets and microclots in ME/CFS [69], as well as altered platelet gene expression profiles [52]. Furthermore, variants in the SERPINA5 gene have previously been associated with ME/CFS [70].

Correlations with several proteins were detected through robust linear regression that were not found through Spearman correlation (Table 3). CSF3, also known as Granulocyte colony-stimulating factor, increases with age in the ME/CFS cohort but is lower with age in the total cohort, perhaps indicating an inflammatory state in the patient cohort. EV chemokine CXCL1, which attracts neutrophils to regions of infection or injury, decreases with age in the total cohort. PFN1, profilin-1, which regulates actin polymerization, is predicted to be higher in males in the total cohort but lower in males with ME/CFS.

We used machine learning classifiers to identify proteins that discriminate between cases and controls. Previously, the proteomics dataset had been subjected to a similar analysis using LASSO, Random Forest, and XGBoost [14]. Seven proteins are common to the top 20 lists of all three machine learning methods. In addition to EV IL2, there were CAMP, IGLV1-47, CRTAC1, LRG1, IGF1, and TUBA1. Four of these were also in the group of 8 proteins that were common to the three methods in the prior study which analyzed only the plasma proteomics data [14]. IGF1 and TUBA1ABC were not in the top 20 when the total cohort was considered in the prior study. Among the seven common proteins, only EV IL2 and CAMP (Cathelicidin AntiMicrobial Protein) were increased in cases vs controls, and both are pro-inflammatory. The significance of a reduction in ILGVI-47 (Immunoglobulin Lambda Variable 1–47) in cases is difficult to predict but could reflect some unknown genotypic effect on susceptibility to ME/CFS. CRTAC1 (Cartilage Acidic Protein 1) is an extracellular matrix protein of unknown function, but improved growth of dermal fibroblasts in vitro, so lower levels could be detrimental. LRG1 (Leucine Rich Alpha-2-Glycoprotein 1) is secreted from hepatocytes and neutrophils, and higher levels are associated with beneficial functions (promoting wound healing) but also with a variety of diseases; thus, the significance of its reduction is unknown [71]. Lower levels of IGF1 (Insulin Like Growth Factor 1) are likely to be unfavorable for health, given its growth-promoting properties and effects on metabolism [72, 73]. TUBA1A, TUBA1B, and TUBA1C genes encode tubulin, an essential component of the cytoskeleton [74]. Tubulin signaling has been found to be disrupted following chemotherapy and is hypothesized to have a role in the neurocognitive impairment that often results following treatment [75].

EV-located IL2 is found in all three lists. IL2 was the only EV cytokine to distinguish cases and controls at q < 0.05 (Fig. 3). In our prior pilot study of EV cytokines in 35 cases and 35 controls, we did not find any significant difference in IL2 between cohorts [25]. Other EV cytokines that featured in the top 20 are VEGF, NGFB, IL15, CXCL8, CXCL10, CCL5, and CCL7, although VEGF, IL15, and CXCL10 did not discriminate cases and controls at q < 0.2, according to Wilcoxon tests (Fig. 3). Plasma cytokines IL7, TNFα, IL12p70, and IL22 were included on one or two of the top 20 lists. While no significant differences in any plasma cytokines were detected following correction for multiple testing, before correction TNFα was increased in cases at p = 0.016 [26]. Previously, Hornig et al. [41], who performed a larger study, with 298 cases and 348 controls, did not find significant differences between cases and controls for these cytokines. The cytokine profiling literature in ME/CFS has not resulted in consistent conclusions regarding altered cytokine levels between ME/CFS and controls.

This work does have some limitations. First, our study has a small sample size, especially given the heterogeneity of the symptoms of the illness and when measuring a large number of variables. The robustness of our findings needs to be verified in more diverse and larger cohorts.

Although ME/CFS has a higher disease burden in females [76] and an increasing number of sex differences in its pathophysiology have been discovered recently [77, 78], we were unable to report disaggregated sex data in our study due to sample size limitations (8 and 9 males compared to 41 and 40 females for the control and ME/CFS populations, respectively). Therefore, statistical comparisons between sexes were not feasible in our current study.

This study examined only peripheral blood and did not analyze other compartments such as cerebrospinal fluid. However, despite a small sample size, abnormalities in proteins of ME/CFS patients have been identified in cerebrospinal fluid studies [9, 46, 79]. Future proteomic research on peripheral blood of ME/CFS patients should strive to establish correlations with these findings.

Here, cytokine measurement in plasma and EVs was performed using different multiplex assays. Specifically, a 61-plex from Affymetrix was used to analyze cytokines in plasma samples, whereas a 48-plex from Biorad was used to measure cytokine content in EVs.

We opted for a precipitation method for EV isolation due to limited sample volumes (500 μl) and to enable analysis of the complete EV population. Using precipitating reagent ExoQuick tends to yield lower purity for EV isolated fractions compared to other methods such as ultracentrifugation and size exclusion chromatography. Future studies comparing these methods in cytokine analysis will be informative to ensure our results are reproducible using other EV isolation methods. Furthermore, EVs were not separated into different fractions by size or by the presence of particular surface molecules to allow analysis of these fractions separately. It is certain that distinct patterns will arise indicating the selective packaging of specific proteins into specific EVs.

It should be noted that the correlations reported in this study do not indicate cause-effect relationships, and further research is required to establish causality. For instance, since the diet of the subjects was not controlled in this study, discrepancies in cytokine profiles between different groups could be attributed to differences in their diets [80‐82]. Thus, we cannot rule out the possibility that dietary factors may have influenced our results.

Ultimately, since this study employed a cross-sectional approach; examining longitudinal changes in EVs would require further exploration. Moreover, one-time sample collection prevents determining whether associations between symptoms and protein profiles in plasma and EVs of ME/CFS patients stem from disease progression. Future research is crucial to establish whether patients with ME/CFS consistently exhibit a specific cytokine signature and disease severity classification over time, or if these factors fluctuate.

This work demonstrates the importance of collecting clinical data to determine whether particular molecules are correlated with the subjects’ conditions, allowing conclusions to be drawn about them even if their median values differ little between cases and controls. We have again demonstrated that cytokine/chemokine signaling networks in the circulation are altered between ME/CFS cases and controls. Finally, we have identified 20 proteins whose levels provided very high sensitivity and specificity for distinguishing ME/CFS and control samples. A more manageable subset of 7 of the 20 proteins still allows considerable separation of patients from controls (AUROC = 0.891, Fig. 8). These findings await confirmation in a larger dataset to determine whether they can be clinically useful for diagnosis or monitoring response to treatment.