Proteomics And Cytokine Analyses Distinguish Myalgic Encephalomyelitis/chronic Fatigue Syndrome Cases From Controls Part 1

Abstract

Background Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, heterogeneous disease characterized by unexplained persistent fatigue and other features including cognitive impairment, myalgias, postexertional malaise, and immune system dysfunction. Cytokines are present in plasma and encapsulated in extracellular vesicles (EVs), but there have been only a few reports of EV characteristics and cargo in ME/CFS. Several small studies have previously described plasma proteins or protein pathways that are associated with ME/CFS.

Cistanche can act as an anti-fatigue and stamina enhancer, and experimental studies have shown that the decoction of Cistanche tubulosa could effectively protect the liver hepatocytes and endothelial cells damaged in weight-bearing swimming mice, upregulate the expression of NOS3, and promote hepatic glycogen synthesis, thus exerting anti-fatigue efficacy. Phenylethanoid glycoside-rich Cistanche tubulosa extract could significantly reduce the serum creatine kinase, lactate dehydrogenase, and lactate levels, and increase the hemoglobin (HB) and glucose levels in ICR mice, and this could play an anti-fatigue role by decreasing the muscle damage and delaying the lactic acid enrichment for energy storage in mice. Compound Cistanche Tubulosa Tablets significantly prolonged the weight-bearing swimming time, increased the hepatic glycogen reserve, and decreased the serum urea level after exercise in mice, showing its anti-fatigue effect. The decoction of Cistanchis can improve endurance and accelerate the elimination of fatigue in exercising mice, and can also reduce the elevation of serum creatine kinase after load exercise and keep the ultrastructure of skeletal muscle of mice normal after exercise, which indicates that it has the effects of enhancing physical strength and anti-fatigue. Cistanchis also significantly prolonged the survival time of nitrite-poisoned mice and enhanced the tolerance against hypoxia and fatigue.

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【Contact】Email: george.deng@wecistanche.com / WhatsApp:008613632399501/Wechat:13632399501

Methods We prepared extracellular vesicles (EVs) from frozen plasma samples from a cohort of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) cases and controls with prior published plasma cytokine and plasma proteomics data. The cytokine content of the plasma-derived extracellular vesicles was determined by a multiplex assay and differences between patients and controls were assessed. We then performed multi-omic statistical analyses that considered not only this new data but extensive clinical data describing the health of the subjects.

Results in ME/CFS cases exhibited greater size and concentration of EVs in plasma. Assays of cytokine content in EVs revealed that IL2 was significantly higher in some cases. We observed numerous correlations among EV cytokines, plasma cytokines, and plasma proteins from mass spectrometry proteomics. Significant correlations between clinical data and protein levels suggest the roles of particular proteins and pathways in the disease. For example, higher levels of the pro-inflammatory cytokines Granulocyte-Monocyte Colony-Stimulating Factor (CSF2) and Tumor Necrosis Factor (TNFα) were correlated with greater physical and fatigue symptoms in ME/CFS cases. Higher serine protease SERPINA5, which is involved in hemostasis, was correlated with higher SF-36 general health scores in ME/ CFS. Machine learning classifiers were able to identify a list of 20 proteins that could discriminate between cases and controls, with XGBoost providing the best classification with 86.1% accuracy and a cross-validated AUROC value of  0.947. Random Forest distinguished cases from controls with 79.1% accuracy and an AUROC value of 0.891 using only 7 proteins.

Conclusions These findings add to the substantial number of objective differences in biomolecules that have been identified in individuals with ME/CFS. The observed correlations of proteins important in immune responses and hemostasis with clinical data further implicate a disturbance of these functions in ME/CFS.

Keywords Myalgic encephalomyelitis/chronic fatigue syndrome, Extracellular vesicles, Plasma, Proteomics, Cytokines

Background

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 worldwide were estimated to experience ME/CFS [4]. Since the SARS-COV2 pandemic, a subset of individuals who have suffered acute COVID-19 have been continuing to experience symptoms [5], and some victims of long-term COVID-19 fulfill the ME/CFS diagnostic criteria described above [6]. Likewise, individuals experiencing Gulf War Illness have symptoms that overlap with both Long COVID and ME/CFS [7]. However, several 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 different 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 a 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 a 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 before 2020 from ME/ CFS subjects and healthy 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 together for multiple statistical analyses. 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 the 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 control subjects at extremely high sensitivity and specificity (AUC=0.947).

Methods

Study population 

A subpopulation of 49 ME/CFS cases and 49 healthy controls from the Chronic Fatigue Initiative cohort [27] were analyzed in the framework of this current study. All cases met the 1994 CDC Fukuda [28] and/or 2003 Canadian consensus criteria for ME/CFS [29]. On the day of blood collection, clinical symptoms and baseline health status were assessed using the Short Form 36 Health Survey (SF-36) [30] and the Multidimensional Fatigue Inventory (MFI) scale [31]. Peripheral blood was drawn in sodium citrate BD VacutainerTM Cell Preparation Tubes and centrifuged to pellet red blood cells. Resulting plasma samples were received from four locations from supervising physicians as shown: Salt Lake City, Utah (Lucinda Bateman), Incline Village, Nevada (Daniel  Peterson), Miami, Florida (Nancy Klimas), and New York City, New York (Susan Levine) and stored at – 80 ℃ and shipped from Columbia University to Cornell University on dry ice and stored at – 80 ℃ before processing for isolation of extracellular vesicles. Written consent was obtained from all participants and all protocols were approved by the Institutional Review Board at Columbia  University Irving Medical Center.

Purification of extracellular vesicles

Extracellular vesicles (EVs) were isolated from plasma samples by precipitation using the ExoQuick™ reagent (System Biosciences, Palo Alto, CA, USA) as previously described [25]. Briefly, plasma samples from each subject were thawed on ice and centrifuged at 3000×g for 15 min at room temperature to remove cells and debris.  Thrombin (611 U/ml) (System Bioscience, Palo Alto, CA, USA) was added and samples were incubated for 5 min at room temperature to remove fibrinogen, centrifuged at 10,000×g for 5 min, and the supernatant was collected. The samples were then incubated with ExoQuick™ for 60 min at 4 °C, centrifuged at 12,000×g for 5 min, and the resulting pellet was resuspended in 250 ul of sterile phosphate buffered saline 1X, pH 7.4. Samples were aliquoted for quantification of cytokines/chemokines and growth factors.

Size and quantification of extracellular vesicles

Concentration and size distribution of isolated EVs were assayed in samples using a NanoSight NS300 instrument  (Malvern, Worcestershire, UK) at the Cornell Nanoscale Science and Technology Facility. Samples were thawed and diluted to 1:2000 in PBS 1X and 1 ml was injected through the laser chamber (NanoSight Technology, London, UK). Tree recordings of 60 digital videos of each sample were acquired and analyzed by the NanoSight NTA 2.3 software to determine the size and concentration of nanoparticles. Results were averaged together.

Immune profiling of plasma and extracellular vesicles

Immune molecules in plasma were previously measured using a magnetic bead-based 61-plex immunoassay (customized ProcartaTM immunoassay, Affymetrix) [26]. The immune profiling of extracellular vesicles was performed at the Human Nutritional Chemistry Service Laboratory at Cornell University using a human 48-plex magnetic bead kit (Bio-Plex Pro Human Cytokine Screening Panel, 48-plex, Bio-Rad). Before analysis, EV samples were treated with Triton 1% to allow the release of encapsulated cytokines [32]. Each sample was measured in duplicate on a MAGPIX® Multiplexing System (Luminex Corp.). For each well, we used the median fluorescence intensity of all beads measured for a given analyte and averaged the two replicates, and results were accepted when the coefficient of variation (CV) was below 15%.

Plasma proteomics

Plasma proteomic profiling was conducted at Columbia University as previously described [14]. Samples from the 49 ME/CFS cases and 49 controls included in this study were run in two batches of 20 samples (11 ME/CFS cases, 9 controls) and 78 samples (38 ME/CFS cases, 40 controls). The 20 samples in the first batch were randomly selected. The cases and controls were frequency-matched on the same matching variables as the total study population. A total of 257 and 279 annotated proteins were measured in the 20-subject sample set and 78-subject sample set, respectively, with an overlap of 207 annotated proteins in both sample sets.

Statistical analysis

All statistical analyses were performed using R version 4.0.2 (2020-06-22) via RStudio. For each protein analyte, non-detectable values were replaced with half of their minimum value. Protein levels were then log-transformed with base 2 and standardized for further analysis. Z scores and P values were calculated for outlier analysis. The non-parametric Wilcoxon signed-rank tests were performed to test the significance of differences (p<0.05) between cases and controls for age, BMI, SF-36 survey scores, and EV sizes and concentrations. The robust linear regression was performed using the lm function in the MASS package for determining the significance of differences for each analyte in control and ME/CFS groups with age, BMI, Irritable Bowel Syndrome (IBS), and sex as confounding variables. Robust linear regression was performed to eliminate contamination with outliers or influential observations. Robust linear regression is a form of weighted least squares regression, and we chose M-estimation with Huber weighting [33, 34] for further analysis.

Principal Component Analysis (PCA) was used to simplify the data and increase interpretability by reducing the dimensionality of the protein levels datasets. PCA was performed using the stats package in R. Spearman’s rank correlation coefficients were also estimated within protein analytes and between proteins and the metadata (age, BMI, sex, SF-36 scores, IBS). Point-biserial correlations were used when one of the variables was binary (e.g., female vs. male, with vs without IBS). Categorical variables were coded as follows: Cohort: control=0; ME/ CFS=1; Sex: female=0; male=1; IBS: no IBS=0; with IBS=1. Throughout, all p values were adjusted for multiple hypotheses using the Benjamini–Hochberg method  (FDR) [35, 36].

A machine learning approach was used to identify variables discriminating the two groups of samples (feature selection). Classification of samples as ME/CFS or healthy controls was carried out by using three supervised learning algorithms: random forest [37] implemented using R’s Random Forest function; XGBoost [38] using R’s xgboost package and the Least Absolute Shrinkage and Selection Operator (LASSO) penalty  [39] applied to logistic regression using the R function glmnet. As features, the algorithms used all 353 protein analytes, EV cytokines, plasma cytokines, and plasma proteomics. Feature importance for each classifier was calculated. For LASSO, the coefficients of “unimportant” features are shrunk to zero, hence feature importance can be evaluated by “percentage” (out of 250 random resampling cross-validation iterations) in which the predictor’s parameter estimate in the best fitting model is nonzero. For random forest, the “Mean Decrease Accuracy (MDA)” of a feature is the decrease in classification accuracy due to randomly permuting the values in that feature. For unimportant predictors, the permutation should have little to no effect on model accuracy, while permuting values of important predictors should significantly decrease it.  Therefore, the greater the importance of a feature, the greater the decrease in accuracy when its values are permuted. Finally, for XGBoost, the metric “Gain” indicates the average gain across all trees that the feature is used in, which describes the relative contribution of each feature.

Feature importance was calculated by the average of over 250 replications of fivefold cross-validation. Protein analytes that were ranked in the top 20 in importance measurements in all three classifiers (Table 5) were fitted as predictors in the same classifiers again. Receiver Operating Characteristic (ROC) curves and area under the curve (AUC) used to optimize feature selection were calculated using the R package caret. The data was log-transformed and auto-scaled before the ROC curves were generated. A lasso penalty is used when many predictors and variables that are important for prediction are selected. Since we were using variables already determined to be important, unregularized logistic regression rather than the lasso penalty was used in  Fig. 8. Average AUCs were calculated with 250 repeats of fivefold cross-validation, which is intended to derive a more accurate estimate of model prediction performance. Feature importances were calculated for each of the three machine learning algorithms.

Results

Study population characteristics

Within the study population, there were 41 females and 8 males and 40 females and 9 males in the ME/CFS   and healthy controls groups respectively (Table 1). All patients who were selected met the 1994 Fukuda definition for ME/CFS. The average age and Body Mass Index (BMI) were similar between ME/CFS and control subjects and also in comparison of sexes between groups (Table 1). Seventy-nine percent of the ME/ CFS patients were able to identify an acute, often like an illness that immediately preceded the onset of the disease, while 20% were unaware of an initiating event and considered their onset to be gradual (Table 1), and 45 out of 49 patients had their illness for more than  3 years. The MFI-20 scores depict the opposing trend of the condition of ME/CFS subjects versus controls, with a higher score reflecting the lower functional level of patients compared to the smaller score of fully functional controls (Table 1, p < 0.001). Furthermore, both the Physical and Mental Component Scores (PCS and MCS respectively) derived from the SF-36 short survey were, as expected, higher in the control group  (p < 0.001, Table 1).

The Principal Component Analysis presented in Fig. 1 was performed on data obtained from the SF-36 and  MFI-20 questionnaires. The first two principal components explained 86.9% (PC-1 75.1%; PC-2 11.8%, Fig. 1a) and 92.6% (PC-1 86.89%; PC-2 5.73%, Fig.  1b) of the total variance within the data set for SF-36 and MFI-20 respectively, and two significant clusters were observed, separating the ME/CFS group from the control group. Neither the season nor the site where the blood was collected could distinguish groups (Additional file 1: Fig. S1).

Size and concentrations of extracellular vesicles are different between ME/CFS and healthy controls

Extracellular vesicles were purified from plasma samples from ME/CFS patients and healthy individuals by precipitation and their size and concentrations were analyzed by Nanoparticle Tracking Analysis (NTA) to investigate whether there were differences between clinical groups. All nanoparticles purified were smaller than 500 nm, most of them being in the typical exosome size range of 30–130 nm [40]. NTA revealed that EV particles' size means differed between healthy individuals (136.2±18.3 nm, range 97–188 nm) and ME/CFS patients (145.3±16.6 nm, range 113–177 nm) (p=0.01, Fig. 2a). The mean total concentration of particles/ml of plasma (controls: 8.0±3.8× 108; ME/CFS: 10.5±3.9× 108, p<0.001, Fig. 2b), the mean concentration of EVs that ranged from 30 to 130 nm in size (controls:4.3±1.8× 108,  ME/CFS:5.3±2.4× 108, p=0.05, Fig. 2c) and the mean concentration of particles greater than 130 nm (controls: 3.7±2.9× 108; ME/CFS: 5.6±2.7× 108, p<0.001, Fig. 2d) also exhibited a statistically significant difference between groups.

Outlier analysis results in the removal of certain subjects’ data from further consideration

We examined the number of outlier analytes across datasets. Any analyte with more than half non-detectable values was discarded, thus 6 of the 61 plasma cytokines were removed. A z-score was calculated for each subject and each analyte. Any subject/analyte pair with a two-sided q-value (p-value adjusted for FDR) less than 0.05 was considered an outlier. The resulting q-values suggested that two ME/CFS patients presented outlier profiles not initially suspected by their clinical features and therefore should be removed from the EV cytokines dataset as they represented 43% and  50% of outliers respectively (21 and 24 outliers out of 48 cytokines). For the plasma cytokines dataset, no subject had a particularly high proportion of outliers, and for plasma proteomics, one ME/CFS patient presented 35% outliers (73 outliers out of 208 plasma proteins) and thus was not used in further analysis.

Certain EV cytokine and plasma cytokine levels differ between ME/CFS and control groups

We investigated differences in levels of analytes between ME/CFS patients and controls using nonparametric signed-rank Wilcoxon tests. Among the EV cytokines, levels of Interleukin 2 (IL2) were signifcantly diferent between controls and patients (q=0.007) and the following 16 EV cytokines exhibited 0.1 < q < 0.2:  IL12P40, TNFα, IL1β, CXCL8, CXCL1, IL15, CCL7, IL17, IL4, GM-CSF/CSF2, IL3, CCL5, NGFβ, IL1α,  IL7, IL1R1. Figure 3 shows boxplots of the log-transformed protein levels of these 17 cytokines. For plasma cytokines [41] and plasma proteomics [14], no analyte was significantly different between cases and controls after correction for multiple comparisons (FDR < 0.2). Detailed p-values, q-values, and the ratios of mean protein analyte level for the ME/CFS group versus controls can be found in the Additional file 2: Tables.

Additionally, we compared sample types within subjects with Principal Component Analysis. A total of 36 common analytes from the 48-plex EV and 55-plex plasma immunoassays were used for this analysis. The percentage of variability explained by each dimension was 46.2% for the first axis and 15% for the second axis, and two significant clusters were observed (Fig. 4).

Numerous correlations exist within and between protein datasets

Spearman correlation analyses were performed between datasets and are plotted as correlograms showing only signifcant correlations with coefcient r ≥0.6 (Fig. 5). A total of 316 positive signifcant correlations were found in ME/CFS subjects and 300 in controls between cytokine levels in EV samples (q<0.01) and 88 and 73 had strong Spearman correlation coefcients (r ≥0.6) in the ME/CFS and control groups, respectively (Fig. 5a). Tirty-four of them were common to both groups (pink squares, Fig.  5a). When correlating plasma cytokines to each other, the ME/CFS cohort had 710 signifcant correlations including 327 at r ≥0.6 (q<0.01), and the control group had 394 with 146 at r ≥0.6 (q<0.01); 136 were common to both groups (Fig. 5b). In both EV and plasma cytokine correlation analysis, no signifcant negative correlations were found, and there was a higher number of positive correlations in the ME/CFS cohort as compared to the healthy individuals (Fig. 5a, b).

We also investigated correlations between the 55 plasma and 48 EV cytokine levels (Additional file 1:  Fig. S2). No negative and few positive significant correlations were found in both group (15 and 13 for ME/CFS and controls respectively at r≥0.5, with 4 common to both groups). Amongst these significant correlations, levels of LIF in EVs correlated with 8 plasma cytokines in ME/CFS (CCL3, IL15, LIF, IL17, IL21, IFNβ, TGFα, and TGFβ) and 5 in the control group (CCL3, IL1α, IL17, IL21, and IFNβ) (Supplemental Fig. 2).

For plasma proteomics, 160 and 130 significant positive correlations were found in the ME/CFS and control groups, respectively, with a Spearman coefficient r greater than 0.8 (q < 0.01) (Fig. 5c) and 42 were common to both groups (pink squares, Fig. 5c). Six pairs of proteins were significantly and negatively correlated in the ME/CFS group only (orange squares, Fig. 5c), with 3 including SERPINA7, and one unique to the control group (SERPINA1/KNG1, r=− 0.82, q < 0.01, light blue square, Fig. 5c).

When analyzing relationships between the plasma proteomics dataset with either the EV cytokines or the plasma cytokines datasets, only one significant correlation was found between an EV protein and a protein assayed by mass spectrometry in the control group (CXCL12-ev/PROZ, r=0.69, q=0.014).

【Contact】Email: george.deng@wecistanche.com / WhatsApp:008613632399501/Wechat:13632399501