Charting the Circulating Proteome in ME/CFS: Cross System Profiling and Mechanistic insights

Charting the Circulating Proteome in ME/CFS: Cross System Profiling and Mechanistic insights | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Charting the Circulating Proteome in ME/CFS: Cross System Profiling and Mechanistic insights August Hoel , Fredrik Hoel , Sissel Elisabeth Furesund Dyrstad , Henrique Chapola , Ingrid Gurvin Rekeland , Kristin Risa , Kine Alme , Kari Sørland , Karl Albert Brokstad , Hans-Peter Marti , Olav Mella , Øystein Fluge , View ORCID Profile Karl Johan Tronstad doi: https://doi.org/10.1101/2025.05.28.25328245 August Hoel 1 Department of Biomedicine, University of Bergen , Bergen, Norway 2 Department of Clinical Medicine, University of Bergen , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fredrik Hoel 1 Department of Biomedicine, University of Bergen , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sissel Elisabeth Furesund Dyrstad 1 Department of Biomedicine, University of Bergen , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Henrique Chapola 1 Department of Biomedicine, University of Bergen , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ingrid Gurvin Rekeland 3 Department of Oncology and Medical Physics, Haukeland University Hospital , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kristin Risa 3 Department of Oncology and Medical Physics, Haukeland University Hospital , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kine Alme 3 Department of Oncology and Medical Physics, Haukeland University Hospital , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kari Sørland 3 Department of Oncology and Medical Physics, Haukeland University Hospital , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karl Albert Brokstad 4 Department of Safety, Chemistry and Biomedical laboratory sciences, Western Norway University of Applied Sciences , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hans-Peter Marti 2 Department of Clinical Medicine, University of Bergen , Bergen, Norway 5 Department of Medicine, Haukeland University Hospital , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Olav Mella 3 Department of Oncology and Medical Physics, Haukeland University Hospital , Bergen, Norway 6 Department of Clinical Science, University of Bergen , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Øystein Fluge 3 Department of Oncology and Medical Physics, Haukeland University Hospital , Bergen, Norway 6 Department of Clinical Science, University of Bergen , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karl Johan Tronstad 1 Department of Biomedicine, University of Bergen , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karl Johan Tronstad For correspondence: karl.tronstad{at}uib.no Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Summary Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating condition often triggered by infections. The underlying mechanism remains poorly understood, and diagnostic markers and effective treatments are presently lacking. We performed aptamer-based serum proteomics in 54 ME/CFS patients and 27 healthy controls and identified 1823 of 7326 aptamers reporting differences between the groups (845 after false discovery rate (FDR) correction). Distinct patterns of tissue- and process-specific changes were seen. There was a broad increase in secreted proteins, while intracellular proteins, e.g. from skeletal muscle, particularly showed reduction. Immune cell-specific signatures indicated immune reprogramming, including a distinct reduction in neutrophil-associated proteins. Focused secretome analysis supported intensified regulatory interactions related to immune activity, inflammation, vasculature, and metabolism. Validation of measurements using antibody-based methods confirmed findings for a selection of proteins. The uncovered serum proteome patterns in ME/CFS patients help clarify a multifaceted pathophysiology and offer a foundation for future therapy and biomarker discovery efforts. Introduction Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating disease that often is linked to infection. Common triggers include Epstein–Barr virus (EBV, infectious mononucleosis), human herpes virus, and more recently, SARS-CoV-2 (COVID-19) 1 – 3 . Using the Canadian consensus criteria 4 , the pre-COVID-19 pandemic prevalence of ME/CFS was estimated to be between 0.2% and 0.8% 5 – 7 . There has been an increase in cases following SARS-CoV-2 8 . Key symptoms include profound fatigue, post-exertional malaise (PEM), sensory hypersensitivity, pain, unrefreshing sleep, and cognitive dysfunction 9 , 10 . The consequences of ME/CFS are severe for both patients and their families, with high socio-economic costs 7 , 11 – 13 . There is an urgent need for more knowledge about the biological mechanisms of ME/CFS to develop effective diagnostic markers and treatments. Although the etiology of ME/CFS remains unclear, several biological manifestations, such as immune system dysregulation, viral persistence, chronic inflammation, and impaired metabolism, have been reported 14 , 15 . We are currently investigating the hypothesis that ME/CFS may involve an autoimmune mechanism leading to vascular dysregulation, causing tissue hypoperfusion and hypoxia, especially upon exertion, and resulting in both short- and long-term effects on energy metabolism 16 , 17 . This mechanism could explain key aspects of symptom generation and fatiguability in ME/CFS, potentially involving exertion-triggered muscle abnormalities, as recently reported in association with PEM in long COVID patients 18 . Human cells express thousands of proteins in a tissue-specific manner 19 , 20 , many of which are released into the blood either by function, as messengers, or due to tissue leak 21 . Pathology in specific parts of the body influences both the patterns of protein expression and the release of proteins into the bloodstream through controlled and uncontrolled mechanisms. Knowledge about circulating protein levels can provide insights into disease mechanisms, tissue regulation, homeostasis, secretory processes, and metabolism 20 . Studies of circulating proteins in the blood of ME/CFS patients have reported abnormal, yet variable, findings related to cytokines and immunological factors 22 , 23 , vascular regulators 24 , and metabolic and muscle-derived messengers 17 , 25 . Further in-depth studies are needed to better understand the underlying pathology. Aptamer microarray proteomics is a discovery platform capable of simultaneously measuring a high number of proteins from small sample volumes, which utilize the protein-specific binding affinity of small single-stranded DNA or RNA molecules (aptamers) 26 . This technology has been used successfully to study circulatory factors involved in aging 27 , kidney disease 26 , diabetes 28 , and other diseases. To our knowledge, it has been employed in only two previous ME/CFS studies, both using relatively small study cohorts 29 , 30 . The overall aim of this study was to identify circulating protein patterns that reveal new insights into processes taking place at cellular, tissue and systemic levels in ME/CFS patients, and investigate the hypothesis of an immune system-related root-mechanism causing impaired microcirculation and energy metabolism. Results Characterization of the subjects We analyzed blood serum from 54 ME/CFS patient biobank samples collected at baseline in two previous clinical intervention trials: RituxME ( ClinicalTrials.gov NCT02229942 , 2014–2017) 31 and CycloME ( ClinicalTrials.gov NCT02444091 , 2015–2020) 32 ( Table 1 , Figure 1A ). The healthy control (HC) group consisted of 27 sex- and age-matched individuals. The blood glucose concentration was not statistically different between the ME/CFS group and the HC group ( Table 1 ). However, the blood concentrations of triacylglycerols (TAGs) and non-esterified fatty acids (NEFAs) were higher in the ME/CFS group. This finding is consistent with our previous comprehensive metabolomics study using samples from the same biobank, which identified patterns of energy strain and context-dependent metabolic phenotypes (metabotypes) in patient subsets 17 . View this table: View inline View popup Download powerpoint Table 1 Group characteristics Download figure Open in new tab Figure 1 Serum proteomics comparing ME/CFS (ME) patients with healthy controls (HC) A) Study Approach: The serum protein concentrations were measured using aptamer-based technology (SomaScan). Exploratory data analysis (EDA) was conducted to identify cellular and tissue-specific impacts, effects on the secretome, and the associated molecular and biological functions involved. Antibody-based measurements were used to validate and expand on findings, with a focus on the immune system and energy metabolism. B) Principal Component Analysis (PCA) with overlay of the ME/CFS and HC groups, and C) projections of associated eigenvalues reflecting the influence of age, sex, BMI, and fasting on the PC1 and PC2 dimensions. D) Volcano plot displaying all the p- and q-significant features. E) The 25 most up- or downregulated protein targets (ranked by fold change) in the ME/CFS patients are categorized according to their functional roles (colors), based on current literature. F) Group comparison of selected significantly affected serum proteins. All statistical outcomes include both uncorrected (p-value) and multiple comparison corrected (FDR, q-value) univariate analyses, adjusted for sex, age, BMI, and overnight fasting. Comprehensive differences between the ME/CFS group and the HC group The serum samples were analyzed using the SomaScan aptamer-based detection platform, yielding a dataset for 7326 aptamers (6494 protein targets, 6409 identified proteins) (SupplData1). In total, as many as 1823 of the aptamers showed statistically significant differences (p < 0.05) between the ME/CFS group and the HC group, with 845 showing q-significant changes (SupplData2). This corresponds to 1610 different proteins (751 q-significant) demonstrating altered serum concentrations. The PCA (Principal Component Analysis) plot with group overlay showed partial separation between the ME/CFS and HC subjects ( Figure 1B-C ; SupplData3), primarily along the PC2 dimension (y-axis, 9.6% explained variance). The groups largely overlapped across the PC1 dimension (x-axis, 14.5% explained variance), indicating that certain elements of intragroup variation are shared between the groups. This makes sense, as PC1 was more influenced by the covariate factors, age, sex, BMI, and fasting state, as indicated by the respective loadings, compared to PC2 ( Figure 1C ; variable, PC1, PC2, p; BMI, 0.943 -0.333, 0.106; age, -0.981, -0.195, 0.381; sex, -0.925, -0.380, 0.091; fasting, -0.830, -0.557, 0.046). Since group separation along PC2 did not seem to be driven by these covariate factors, this may point to more disease-specific changes. Accordingly, the major PC2-driving proteins (i.e. with high loading) expressed more statistically significant differences between the two groups, compared to major PC1-driving proteins. Additional investigations aimed at separating disease-specific effects from covariate influences are included in the forthcoming analyses. More than 60% of the affected aptamers showed lower protein abundance in the ME/CFS patients (62% using p < 0.05, 1127/1823; 73% using q < 0.05, 619/845), while the remainder showed higher abundance (38% using p < 0.05, 696/1823; 27% using q < 0.05, 226/845) ( Figure 1D ). Initial key observations related to the hypothesized pathomechanism The list of the 25 protein targets showing the sharpest increase or decrease in ME/CFS patients indicated specific changes of key interest ( Figure 1E ). The proteins that showed the most decline were predominantly intracellular proteins, such as histones from the nucleus and enzymes involved in metabolic pathways. In contrast, many proteins involved in secretory and systemic regulation of the immune system and metabolism showed significant increase. Additionally, several factors related to vascular function were among the most affected proteins, either increased or decreased. Individual data are shown for a selection of the most significantly affected proteins ( Figure 1F ). Among the proteins that showed the most comprehensive reduction were a group of histones (H1x, H2.1, and several H2B and H2A types) ( Figure 1E-F ). These are normally nuclear proteins but can be released by activated neutrophils as part of neutrophil extracellular traps (NETs) 33 . Other proteins showing prominent decreases included several enzymes related to cellular energy metabolism, such as glyceraldehyde-3-phosphate dehydrogenase (GAPDH), fructose-bisphosphate aldolase A (ALDOA), and L-lactate dehydrogenase B chain (LDHB). We also observed significant reductions in several proteins associated with vascular function and responses to cellular oxygen deprivation, including vimentin (VIM) 34 , anthrax toxin receptor 2 (ANTXR2, also known as capillary morphogenesis gene 2 (CMG2)) 35 , coxsackievirus and adenovirus receptor (CXADR) 36 , and ATP synthase inhibitory factor subunit 1 (ATP5IF1) 37 . Other reduced proteins included the neuron protein Transgelin-3 (TAGLN3), gamma-interferon-inducible protein 6 (IFI16), and cartilage oligomeric matrix protein (COMP). The proteins showing the greatest increase in the ME/CFS patients included several factors associated with immune responses ( Figure 1D-F ), such as malignant T-cell-amplified sequence 1 (MCTS1) 38 , 39 , complement factor D (CFD), complement component C6 (C6), and the cytokine IL26. There were also significant elevations in factors involved in metabolic regulation, including fatty acid-binding proteins 3 and 4 (FABP3, FABP4), which are considered metabolic stress factors when released into the blood 40 . Furthermore, there were significant increases in cellular retinoic acid-binding protein 2 (CRABP2), a member of the fatty acid-binding protein family; the adipokine and immune cell chemoattractant retinoic acid responder protein 2 (RARRES2; chemerin) 41 ; and endothelial cell-derived lipase (LIPG), which is involved in lipoprotein metabolism and vascular biology 42 as well as immune activation 43 . Other increased proteins included endoplasmic reticulum resident protein 29 (ERP29), transmembrane emp24 domain-containing protein 9 (TMED9), pseudokinase FAM20A, which may function in hematopoiesis, and soluble calcium-activated nucleotidase 1 (CANT1). Throughout our exploration of the data, we did not identify what we regard as statistically and mechanistically supported biomarker candidates. Yet we believe that the findings may shed light on biological mechanisms that can be targeted for such purposes. Influence by covariates and lack of physical activity Covariates such as sex, age, BMI, and diet have significant impact on the composition of circulatory proteins in blood. Furthermore, in ME/CFS and other chronic diseases, the lack of physical activity (deconditioning) may cause systemic effects that influence the levels of blood protein. To evaluate the influence of sex, age, BMI, and overnight fasting on the affected proteins, we investigated the percentage of variance explained by each of these covariates compared to the ME/CFS diagnosis ( Figure 2A , SupplData4). The 30 protein targets with the highest variance explained were selected for each covariate in the linear regression model. Overall, the proteins highly influenced by the ME/CFS diagnosis showed relatively small additional influence from the tested covariates, which suggest that these are disease-specific alterations. This included several of the aforementioned proteins, such as histones (H2AZ1, H1-10, and others), MCTS1, VIM, and LDH. In comparison, the covariates showed associations with separate, yet partially shared, sets of proteins. Among the proteins most affected by several covariates were metabolic hormones, such as leptin, FABP3, and FABP4. Altogether, this analysis identified protein variation caused by sex, age, BMI, and overnight fasting, and separated this from protein alterations specifically observed in ME/CFS patients. Download figure Open in new tab Figure 2 Analysis of influence by covariate factors A) Analysis of differentially expressed features, showing the proportion of variance explained by each covariate (ME, sex, age, BMI, and fasting state). The top 30 most explained features per covariate are shown. B) Semi-partial Pearson correlation network displaying associations between molecular features and ME/CFS diagnosis (ME), metabotype (represented by metabotype M1), and SF-36 physical function (SPF36), adjusted for sex, BMI, overnight fasting, and age. Color intensity indicate correlation strength (red = positive, green = negative). C) Venn diagram displaying the overlap of changed features related to ME/CFS diagnosis, metabotype (M1) and physical function (SF-36PF score, SPF36). D) Go Ontology Enrichment analysis of biological processes within network-derived feature clusters. Dot size represents protein count, and color indicates significance (p-value). To evaluate the extent of changes associated with physical function level and/or metabolic adaptations secondary to the ME/CFS diagnosis, we used a semi-partial Pearson correlation analysis adjusted for the covariates. This analysis identified associations between serum protein concentrations and the ME/CFS diagnosis, the metabolic phenotype (metabotype; we used metabotype M1 as reference 17 ), and the SF-36 physical function score (SF-36PF) ( Figure 2B-D , SupplData5). Using the entire cohort of ME/CFS and HC individuals, a total of 924 aptamers were significantly associated with the ME/CFS diagnosis. Of these, 775 were only associated with the ME/CFS diagnosis and not influenced by the metabotype nor the SF-36PF score. Enrichment analysis indicated an impact on annotated functions such as protein localization to the plasma membrane, Golgi transport, and endosomal transport ( Figure 2D ). Separately, the metabotype and the SF-36PF score influenced the levels of 1015 and 726 aptamers, respectively. The 714 aptamers showing specific relationship with metabotype indicated an impact on the regulation of hormone secretion and the negative regulation of smooth muscle cell proliferation. The 451 aptamers specifically influenced by SF-36PF showed enrichment towards the regulation of protein phosphorylation and the positive regulation of nervous system development. As expected, a relatively high number of proteins (216 aptamers) were simultaneously associated with both metabotype and physical function, but only 5 of these demonstrated additional influence by the ME/CFS diagnosis. A total of 149 aptamers showed simultaneous associations between the ME/CFS diagnosis and either the metabotype category or SF-36PF, or both. Although this analysis does not establish causality, the comprehensive changes found to be unrelated to physical activity argue against deconditioning as a driving factor for the serum proteome changes in the ME/CFS group. Instead, they indicate fundamentally distinct changes that are not explained by other known covariates, and therefore disease-specific in this context. Cellular proteins in circulation - release patterns Cellular proteins enter the bloodstream through various routes, involving both passive and active release mechanisms, from the cell to the extracellular compartment. The contextual roles may include cargo transport, secretion, excretion of waste, and cell damage. We reasoned that changed patterns of proteins originating from different cellular compartments could reveal key information about the underlying mechanisms. For instance, a uniform pattern for enzymes normally found inside cells (e.g., LDH and CKM) would likely reflect events related to cell damage and tissue turnover, rather than changes in the regulation of cellular protein expression. To investigate this notion, we used subcellular compartment annotations based on predicted protein structures available via the Human Protein Atlas (HPA). Of the overall 7326 aptamers, 53.4% (3911) were classified based on their protein targets as ‘intracellular,’ 25.9% (1900) as ‘membrane’ (including membrane-associated), and 17.6% (1292) as ‘secreted’ proteins, while 3.0% (223) remained unannotated ( Figure 3A , SupplData2). Among the 1823 affected aptamers, a larger fraction (62.9%) belonged to the intracellular class, indicating a disproportionate effect on this protein class ( Figure 3B ). In fact, among all the aptamers that showed a decrease (1127), 74.9% (858) belonged to the intracellular protein class, whereas the membrane and secreted protein classes represented 12.8% (144) and 8.0% (90), respectively. In contrast, the protein targets that showed an increase (676) had significantly higher proportions in the membrane and secreted protein classes (30.9%, 209/676 and 26.5%, 179/676, respectively), and fewer in the intracellular class (42.6%, 288/676). Viewed differently, nearly 75% of the affected intracellular protein targets showed lower abundance in ME/CFS serum, compared to only 30-40% in the membrane and secreted protein classes, which exhibited significantly larger proportions of proteins with increased levels. In summary, there was a clear pattern of reduced release of intracellular proteins into the blood, accompanied by increased release of membrane and secreted proteins, in the ME/CFS patients. Download figure Open in new tab Figure 3 The subcellular origin of the affected proteins. The detected protein targets were classified into four categories based on their annotated subcellular localization (Human Protein Atlas): “intracellular,” “membrane” (membrane + membrane-associated), “secreted” proteins, and the remaining unannotated (“#N/A”). The accounting numbers indicate the counts and percentages of A) all the detected protein targets and B) only the affected proteins targets. We also examined mitochondrial proteins using the MitoCarta 3.0 library 44 . We found detectable amounts of 445 mitochondrial proteins in the serum samples (SupplData6). Approximately 20% (87) of the mitochondrial proteins showed lower blood concentrations in the ME/CFS group compared to the HC group, whereas only 7% (29) showed increase. Tissue and immune cell specific footprints To evaluate the impact on major organs releasing proteins to blood, we employed tissue-specific protein panels (HPA) for the brain, liver, intestine, skeletal muscle, lymphoid tissue, and bone marrow ( Figure 4A , SupplData7). Overall, a larger fraction of tissue-specific proteins from the brain, intestine, liver, and lymphoid tissue in ME/CFS patients, had increased compared rather than decreased ( Figure 4B ). In contrast, there was widespread reduction for proteins from skeletal muscle and bone marrow. Furthermore, when examining the subcellular origin, there were reduced levels of intracellular proteins particularly from skeletal muscle, brain, and lymphoid tissue. The pattern was different for secreted proteins, which showed primarily increased release from all the addressed tissues, except from bone marrow. The bone marrow panel showed decreased levels both for intracellular and secreted proteins in the serum from ME/CFS patients compared to the HC group. The liver and intestine panels did not indicate a clear reduction of intracellular proteins, but like most other tissues, the affected secreted proteins tended to be increased in the ME/CFS group. In summary, there was a pattern of increased secretory activity across the tissues, except for bone marrow, accompanied by reduced release of intracellular proteins from skeletal muscle, lymphoid tissue, bone marrow and brain. These coordinated tissue-level changes may support a model of an underlying root mechanism causing regulatory and adaptive systems-level responses. Download figure Open in new tab Figure 4 Tissue and immune cell-specific footprints A) Tissue-related aspects were investigated using annotated protein panels specific to selected tissues and cellular compartment (HPA). B) For each protein panel, the proportion of affected proteins (up/down) was calculated relative (%) to the total number of proteins in the panel that were detected (overall), and after further division into cellular compartments. C) Plots showing the most affected proteins per tissue type (ranked by fold change). C) Volcano plots of immune cell type-specific expression panels (HPA). More detailed investigations of the tissue-specific patterns were undertaken to obtain deeper insights into the homeostatic and functional states of affected organs and cell types. Figure 4C displays the proteins that exhibited the most significant differences between ME/CFS patients and healthy control subjects, categorized by tissue. For bone marrow proteins, proteins such as PADI4, BPI, and MPO, had a sharp reduction, which may indicate altered granulocyte/neutrophil cell function. While the brain and skeletal muscle-specific panels also showed relatively extensive reductions, the pattern was not as clear as for the bone marrow panel. Despite the lower abundance of many skeletal muscle proteins, there was a significant increase of myoglobin (MB), which may indicate a nuanced muscle pathology, rather than major muscle breakdown. For proteins from the intestine, liver, and lymphoid tissue, there was a more balanced pattern of increase and decrease. Therefore, even without detailing the specific functions of the proteins involved, ME/CFS seems to infer tissue-specific impacts on the serum proteome, potentially reflecting overall changes related to tissue homeostasis, turnover and function. To specifically address aspects of immune dysregulation, we used annotated protein panels (HPA) specific to B-cells, T-cells, NK-cells, dendritic cells, monocytes, and granulocytes ( Figure 4D ). The effects associated with these cell types pointed to possible impacts on immune cell interactions, coagulation, and inflammation. As previously mentioned, multiple proteins associated with granulocytes showed significantly lower levels in the ME/CFS group compared to the HC group, such as MPO and BPI. The reduced amount of granulocyte proteins was not associated with abnormally low neutrophil counts (the most abundant type of granulocytes) or other leukocyte types in the patients (DocumentS1, Table S1). Furthermore, comparing a list of proteins associated with neutrophil granules and stimulated neutrophil protein release 33 45 , we found that about 40% or more of the proteins reported to be released by activated neutrophils showed lower serum concentrations in the ME/CFS group compared to the HC group, suggesting a suppressive effect on overall neutrophil activity. Secretory processes and functional changes To further expand our investigations into the apparent stimulation of secretory processes and the impact it may infer related to ME/CFS, we utilized a revised protein-centric list of the predicted human secretome (secreted proteins) 46 , which annotate both tissue-specific expression and functional roles of the proteins ( Figure 5A , SupplData8). Across the total of 2100 aptamers targeting 1654 different secretome proteins, 454 aptamers showed significant differences between the ME/CFS group and HC group. In sharp contrast to initially classified intracellular proteins, the majority was increased (61%, 278 aptamers). Download figure Open in new tab Figure 5 Effect on the secretome and related biological processes A) Secreted proteins were classified according to their tissue-specificity and biological functions 46 . B) Enrichment analysis showing the influence of ME/CFS on the nine tissue classes of secreted proteins, displaying the total and affected protein counts. C) A detailed overview of the effects on the blood protein class according to functional annotations. D) Volcano plots showing all affected secreted proteins per biological function, based on the entire secretome dataset. E) Plots showing the most affected proteins per biological function (ranked by fold change). ABBREVIATIONS: Intracellular and membrane proteins (IC&M), Extracellular matrix (ECM), Male reproductive system (mRS), Female reproductive system (fRS) - Classical plasma proteins (CPP): Acute phase (AP), Apolipoprotein (Apol), Blood coagulation (BC), Complement pathway (AP) - Enzymes (Enz): Enzyme (Enz), Inhibitor (Enzi) - Hormones (Hor): Growth factor (GF), Hormone (H), Neuropeptide (NP) - Immunity and defense (I&D): Defense (Def), Immunoglobulin (Ig), Immunity (Im) - Inflammation (Infl): Chemokine (Chk), Cytokine (Cyk), Interferon: (Intf), Interleukin (Intl) - Transport (Tr): Receptor (Rec), Transport (Tr) - Other: Cell adhesion (Cad), Transcription (Trsc), Developmental protein (DP) The applied annotation classifies nine groups of secreted proteins according to their roles: blood proteins, proteins secreted locally in six different tissue compartments (brain, GI tract, male tissues, female tissues, unknown location, other tissues), proteins forming the extracellular matrix, and one class of intracellular and membrane-bound proteins ( Figure 5B ). For the extracellular matrix proteins and the intracellular and membrane-bound secretome proteins, there were relatively balanced numbers of aptamers that showed increase and decrease. In contrast, a pattern of predominant increase was seen for all the classes reflecting specific tissues (blood, brain, GI tract, male tissues, female tissues, and other tissues), indicating a systemic impact involving many organs. The blood secretome class is crucial in the systems-level control of homeostasis, transport of nutrients, inflammatory response, defense mechanisms, hormone regulation, and many other functions. A large fraction of the secreted blood proteins is known to be released from the liver, whereas others are generated either by blood cells or more generally across all tissues. In total, 197 of 838 aptamers targeting blood secretome proteins showed significant changes, with 63% (124) showing increased concentrations and 37% (73) showing decreased concentrations. The functional annotation indicated mixed impacts related to classical plasma proteins, enzymes, hormones, immunity and defense, inflammation, and transport ( Figure 5C ). To obtain a systems-level scope, functional annotation was applied to the entire secretome dataset ( Figures 5D and 5E ). This clearly showed a skewed pattern of increase for affected secretome proteins involved in coagulation, complement pathways, and inflammation (chemokines, interleukins). In contrast, the enzyme class showed a predominant decrease, which may relate to the overall decreased release of intracellular proteins in general ( Figure 3 ), and especially from bone marrow (granulocytes and monocytes) ( Figure 4 ). The other secretome classes, including hormones, immunity & defense, transport, and others, expressed mixed effects, giving a relatively balanced picture of increase and decrease, possibly reflecting different aspects of physiological regulation. We performed a ligand-receptor interaction analysis to elucidate possible patterns of regulation that could be related to the pathomechanism of ME/CFS (DocumentS1, Figure S1). Overall, we found an overweight of cell adhesion and cytokine-cytokine receptor type ligand-receptor interactions. Interestingly, four members of the Ephrin subfamily A receptors (EPHA) and their ligands (EFNA) displayed concordance, supporting the involvement of altered EPHA-EFNA signaling in ME/CFS, as previously suggested 29 . Furthermore, there was a positive concordance between Fibroblast growth factor receptor 3 (FGFR3) and EPHA, IL6R with the CNTF and MPZ ligands, and FLRT3 with UNC5B and UNC5D. FAP and PAM had negative concordance, along with other interactions. Based on current knowledge, it seems likely that the identified ligand-receptor interactions may play a role in ME/CFS pathology through their impact on metabolic regulation, tissue development and repair, inflammation, and angiogenesis. Validation and expansion of serum proteome findings using antibody-based methods To validate and expand on findings from the aptamer-based analyses, we used antibody-based methods (ELISA and Luminex) to measure a panel of serum proteins related to immunity, inflammation, coagulation, and stressed energy metabolism. Of the 77 proteins measured on the Luminex platform (ME/CFS, n = 83; HC, n = 48), 49 proteins expressed quantifiable serum concentrations at the group level (SupplData9). There was a reasonably good correlation between the antibody-based and aptamer-based measurements, and importantly, significant effects in ME/CFS patients versus HC subjects were generally reproduced ( Figures 6A-B ). Thus, the selected antibody-based measurements validated key findings from the aptamer-based analyses. However, the increase in myoglobin was not reproduced by the antibody-based method. A selection of significant differences between ME/CFS and HC based on the Luminex data, are shown in the heatmap in Figure 6C , which also compare the serum protein levels according to metabotype (metabotype 1, 2, and 3) 17 . Metabotype-specific differences in serum protein concentrations were found particularly for metabolic hormones, such as the increased FABP4 seen in both metabotype 1 and 2, whereas increased insulin and leptin were primarily observed in metabotype 2. The effects on immune system-related proteins generally remained relatively similar between the metabotype subgroups, such as reduced MPO and TGFα. To this end, two exceptions were CCL24, which expressed a specific increase in the metabotype 1 subgroup, and IL-8, which showed a specific decrease in the metabotype 3 subgroup. Download figure Open in new tab Figure 6 Targeted validation and expansion of key findings Antibody-based measurements were used to validate and expand on key outcomes from aptamer-based serum proteomics. Antibody-based measurements (Luminex) were performed of a panel of 49 serum proteins related to immunity and metabolism, in 83 ME/CFS patient and 48 HC subjects. A) and B) show the correlation analysis between antibody-based and the aptamer-based analysis per protein. F) The heatmap display significantly affected proteins (antibody-detected), and comparison between the three ME/CFS metabotypes. C) Additional ELISA analysis of FGF-21, GDF15 and C-peptide was performed in larger cohorts (n=212 ME/CFS, n=66 HC). We performed additional conventional ELISA measurements of two metabolic stress hormones, FGF-21 and GDF15, in addition to C-peptide, which indicate insulin production, using a larger cohort of subjects (n=212 ME/CFS, n=66 HC) ( Figures 6D-F ). FGF21 and C-peptide were not included in the previous aptamer-based analysis. For GDF15, the ELISA analysis showed no difference between the ME/CFS and HC groups, which supported the finding of the aptamer-based analysis ( Figure 6E ). For FGF-21, the serum concentration was higher in the ME/CFS group compared to the HC group ( Figure 6D ). When comparing by sex, the increase of FGF-21 was statistically significant in women, but not in men. The patients in the metabotype 2 subgroup had significantly higher serum concentrations of FGF-21 compared to the two other subgroups. While the serum concentrations of GDF15 and C-peptide were not significantly different between the ME/CFS and HC groups, irrespective of sex, also these showed higher levels in the metabotype 2 subgroup compared to metabotypes 1 and 3. Discussion This serum proteomics study identified comprehensive differences in circulatory protein concentrations between ME/CFS patients and healthy individuals. Through the investigations of the data, we aimed to position the observed patterns of changes within the mechanistic landscape of tissue involvement and functional impact, as summarized in Figure 7 . Download figure Open in new tab Figure 7 Impacts of serum proteome changes in ME/CFS This illustration provides an overview of the key findings from our serum proteome study in ME/CFS, highlighting tissue-specific impacts, secretome alterations, and our interpretations: 1) Tissue-related effects: The analysis revealed distinct patterns for tissue-specific proteins from the brain, intestine, liver, skeletal muscle, and lymphoid tissue. ME/CFS patients showed increased levels of secreted proteins from these tissues, while intracellular proteins, particularly from skeletal muscle and bone marrow, were reduced. There was a specific reduction in the serum levels of proteins released from neutrophils, suggesting altered function of this cell type. 2) Secretome effects: The secretome analysis indicated an overall elevated state of secretory activity in ME/CFS patients. A pattern of increase was seen for proteins involved in coagulation and complement activation, chemokines, cytokines, and hormones, whereas enzymes showed a reduction. 3) Interpretation: The altered protein patterns support the notion of a root mechanism involving autoimmunity and/or immune dysregulation, and consequent roles of low-grade inflammation, tissue hypoperfusion, and metabolic alterations in ME/CFS. We used two main approaches to uncover multiscale insights about the changes occurring in the body due to ME/CFS pathology. First, we analyzed the entire serum proteome dataset to assess systemic, tissue-specific, and immune cell-related impacts. Second, we focused on secreted proteins to examine changes in functional aspects such as signaling, transport, immune defense, and metabolism. We did thorough analyses to characterize the influence of sex, age, BMI, and fasting state, and it was confirmed that these covariates could not explain the changes observed in the ME/CFS group compared to the HC group. Furthermore, the comprehensive changes found to be unrelated to physical function argue against deconditioning as a driving factor for the findings in the ME/CFS group. However, this does not exclude the possibility that some of the covariates may influence biological mechanisms involved in symptom generation and maintenance, such as metabolic dysregulation. This is possibly exemplified by the worse physical function of the metabotype 2 subgroup 17 and its association with relatively high levels of insulin and leptin, combined with specific increases in metabolic stress hormones such as FGF21 and GDF15 ( Figure 6 ). Reduced protein release from muscle The widespread decrease of intracellular proteins released from skeletal muscle to blood may have different explanations. Possible systematic variation, such as dilution effects due to possible blood volume changes in ME/CFS 47 , seems unlikely based on the specificity of the findings related to different tissues and processes. It could be speculated that reduced leakage of muscle proteins into the bloodstream could be associated with low physical activity and less muscle mass, but this is not convincingly supported since the data were adjusted for age, BMI, and sex, which indirectly integrates variation in muscle mass and activity. Yet, we did observe a reduction in COMP, a component of cartilage matrix that temporarily increases in serum upon physical exercise 48 . Alternative explanations may involve aspects of skeletal muscle tissue maintenance and protein washout, and potentially, impaired tissue perfusion causing reduced protein flux from the interstitial fluid to blood. Given that different muscle proteins have different release pathways and clearance dynamics, it seems plausible that a pathomechanism involving vascular dysfunction and microvascular shunting could lead to reduced drainage of cellular protein remains from the tissue in ME/CFS patients. If this is true, the widespread reduction in intracellular muscle proteins in blood may be linked to muscle abnormality and key symptoms in ME/CFS, such as pain, fatigue and PEM, through circulatory and vascular dysfunction 18 , 49 – 51 . Secreted proteins and their functional impact The secretome provides an opportunity to interpret regulatory and functional impacts, as many of these proteins have established extracellular roles as signaling molecules, enzymes, structural proteins, transport proteins, and defense proteins. Our data indicated an elevated overall state of secretory activity in ME/CFS patients, with 62% of the affected secreted proteins showing increased concentrations. Many of these proteins were functionally annotated to coagulation, complement activation, chemokines, interleukins, hormones, enzyme inhibitors, and receptors. Increased blood levels of hormones and cytokines related to inflammation have been observed previously in individuals with ME/CFS, including IL-1, IL-6, IL-7, IL-17, and TNFalpha, although there is some variation between the available findings 22 , 52 , 53 . Additionally, proteins not classified as secreted may act as circulatory messengers when present in blood, such as the tissue-specific FABP types, which function as fatty acid transporters inside cells and as circulating stress messengers when released from cells 54 . In summary, the pattern of elevated secretory activity in the ME/CFS group is mechanistically consistent with immune dysregulation, inflammatory responses, and metabolic stress. Immune cell changes and attenuated neutrophil function Comparison of immune cell-specific protein panels between ME/CFS patients and healthy controls suggested that multiple proteins involved in the functions and interactions of B-cells, dendritic cells, monocytes, NK-cells, and T-cells are affected by the disease. The most increased protein was malignant T cell-amplified sequence 1 (MCTS1), an oncogene originally found in human T-cell lymphoma that recently was identified as a potential biomarker for COVID-19-related thrombosis 39 . Immune dysregulation, as reported in multiple studies 15 , 16 , 55 , may be mechanistically associated with an underlying autoimmune pathomechanism, the overall state of elevated secretory activity, and increased levels of factors involved in inflammation, coagulation, and complement activity. Similar observations have previously been highlighted in relation to ME/CFS and long COVID 15 , 56 . To this end, changes in coagulability, platelet hyperactivation, and fibrinaloid microclot formation has been found in ME/CFS, although not to the same extent as in Long COVID 57 . A recent single-cell transcriptomics study of peripheral blood mononuclear cells (PBMCs, which does not include neutrophils) found both monocyte dysregulation and platelet hyperactivation in ME/CFS patients, although the abnormal platelet state was less pronounced 24 hours post-exercise 58 . Regarding the reduction of bone marrow proteins, decreased protein release from neutrophils appears to be a plausible explanation. Many of the granulocyte-related proteins found to be decreased in the patients are associated with neutrophil granules, stimulated secretion, and NETs, suggesting that changes in neutrophil maturation and/or activation states occur in ME/CFS 59 . These cells release DNA and proteins such as MPO, BPI, histones, and granular proteins when activated upon infection. The release of these proteins is not restricted to the NET process, and other aspects of neutrophil activity and phenotypes may be involved 60 . Since standard clinical blood cell counts did not indicate detectable abnormalities in the circulating numbers of the main leukocyte classes in the ME/CFS patients, the proteomics data may suggest that functional changes are present, which may include exhaustion or suppression of neutrophil activity. This represents a mechanistic element that also is likely to involve other immune cell types, as well as cells in the blood vessel walls 61 . Selective increase in myoglobin Based on the overall pattern of lower serum concentrations of many muscle-related proteins, the selective increase in myoglobin is of a certain interest, as it suggests muscle tissue oxygenation as a potential contributing factor. Nevertheless, as the increase in myoglobin was not reproduced by the antibody-based method, concern needs to be taken for the interpretation, and further investigations are required. Aptamer-based detection is considered a stable proteomics platform that is less susceptible to protein degradation 62 ; however, it remains a semi-quantitative method with some limitations. Myoglobin in blood is a sensitive marker for muscle injury, but since the increase in ME/CFS patients was not accompanied by increases of other markers such as CKM or LDH, it does not seem to reflect tissue damage or differences in muscle mass in this case. In support, others have found reduced blood levels of creatine kinase in ME/CFS patients 25 . Instead, it may be speculated that the possible specific increase in myoglobin could be mechanistically associated with tissue hypoxia adaptation, serving as a compensation that contributes to improved muscle oxygenation. The primary function of myoglobin is to store and facilitate the transfer of oxygen from the cell membrane to the mitochondria inside muscle cells. This may align with our finding that serum from ME/CFS patients contain factors that seem to induce compensatory increases in mitochondrial respiration in muscle cells 63 . Furthermore, this may involve elements of physiological mitigation that could be beneficial in resting ME/CFS patients, when blood lactate levels usually are within the normal range. However, upon exertion, the limitations become apparent, as indicated by the abnormal rise in lactate concentration at low workloads 64 . Vascular dysregulation and hypoxia response Several proteins associated with microcirculation and hypoxia response showed significant effects in ME/CFS patients. There was a reduction in VIM, a structural intracellular protein expressed by various cell types, which interacts with proteins such as CD44 on the endothelial surface 34 . Reduced levels of ANTXR2, which mediates endothelial regulation 35 , and CXADR, which mediates endothelial cell responses to shear stress 36 , were also observed. Additionally, the reduced level of ATP5IF1 may indicate impaired protection against mitochondrial stress during oxygen deprivation 37 . Regarding proteins that showed an increase in the patients, LIPG is an endothelial protein involved in lipoprotein metabolism and vascular function 42 , which is released during immune activation 43 , and FAM20A, which has a possible function in hematopoiesis as secreted signaling protein 65 . Several studies have documented endothelial dysfunction in ME/CFS patients 66 – 68 . The endothelium forms the inner lining of blood vessels and plays important roles in the control of blood flow, immune reactions, and metabolism. Endothelial cell functions are regulated by circulating substances and interactions with other cells in the microenvironment, such as blood cells and smooth muscle cells 69 . The contractile activity of the vessel wall (vascular tone) is a major determinant of blood flow through tissues, but the endothelial surface is also an important site of platelet and immune cell adhesion, aggregation, and activation. Oxygen-dependent mitochondrial ATP production is also a determinant of endothelial cell control of vascular tone 70 , and red blood cell function and deformability 71 . Capillary alterations in skeletal muscle and changes in hypoxia-related blood factors were recently reported in long COVID patients 72 , 73 , and roles of immune dysfunction, inefficient oxygen transport and inflammatory disequilibrium have been proposed as drivers of persisting symptoms 74 , which may be relevant for ME/CFS. This notion is further supported by the activation of the inherent hypoxia response in muscle tissue, as the expression of WASF3, a target gene for the HIF1A transcription factor, was increased in ME/CFS patients 75 . Thus, impaired vascular flow and tissue hypoxia, with a role in endothelial dysfunction, may explain the interrelated patterns of metabolites and proteins that we observed in ME/CFS patients. Metabolic stress and energy metabolism Our findings complement previous reports suggesting the involvement of serum proteins that signal metabolic stress in the context of ME/CFS, such as GDF-15 76 , FGF-21, resistin, leptin, and HGF 22 , 52 . FGF-21 and GDF-15 are metabolic stress hormones, and increased levels have been found in conditions such as mitochondrial myopathies, type 2 diabetes, and hypoxia. Some hormones involved in metabolic adaptations showed associations with the previously described metabolic phenotypes in ME/CFS patients 17 . For instance, based on the expanded targeted validation measurements, c-peptide (an insulin marker), leptin, and FGF21 were particularly increased in the metabotype 2 subset, whereas FABP4 only showed a significant increase in the metabotype 1 subset. In the normal healthy context, increased blood levels of so-called “exerkines,” including proteins such as FGF21 and GDF15, and other molecules such as lactate, free fatty acids (FFAs), and catecholamines, are associated with the physiological effects of exercise 77 . These messengers act across multiple organ systems. Our findings in ME/CFS patients may align with a persistent state of elevated energy strain, which may reduce physical performance and prevent adequate restitution after exertion. Mechanisms causing tissue hypoperfusion could trigger of such metabolic stress 78 , as supported by the link between reduced oxygen extraction and persistent exertional intolerance in metabolic myopathies 79 , in ME/CFS 80 , and after COVID-19 81 . Limitations of the study There are some limitations of the study that should be considered when evaluating the findings. The aptamer technology provides measurement of relative protein levels, and findings of high interest should be verified using additional high-confidence methods. We found a reasonably good correlation between the aptamer-based proteomics analysis and the expanded analysis using antibody-based methods in our study. However, the study sample size is relatively small, although it represent one of the most comprehensive proteomics studies in this patient group. When using protein annotations to track functions and tissue locations, it is important to be aware that few proteins are exclusively specific to a given process or organ and may play different context-dependent roles. Therefore, alternative interpretations of the findings may be relevant. We have mitigated some of these limitations by primarily focusing our interpretations on patterns of changes related to different tissues and processes, rather than single protein effects. For some proteins, different aptamers detected different versions or domains of the same polypeptide. These may theoretically be utilized for purposes of internal validation or investigations of structural changes or peptide cleavage. Perspectives There are several ways in which we believe this work could support new research directions. First, it identifies specific cells, organs, and processes affected in ME/CFS, which may guide the development of diagnostic markers and therapeutic strategies. These insights may also be relevant to related syndromes, such as long COVID and fibromyalgia. Second, it provides a mechanistic landscape that can be used to contextualize previous and future findings within a broader (patho)physiological framework - for example, in relation to potential risk factors identified through multi-omics analyses 82 , 83 . Third, it may inform the development of relevant laboratory models for ME/CFS, including through systemic comparisons 84 , the selection of appropriate cell types, and in vitro recapitulation of key mechanistic features 63 . Conclusion Our findings characterize a pathophysiology in ME/CFS composed of interrelated elements, including immune dysregulation, vascular dysfunction, metabolic stress, and chronic inflammation. The observed multiscale patterns of change converge on a mechanism involving coordinated immune, vascular, and metabolic disturbances. We hope these insights will support further research into disease mechanisms, therapeutic targets, and the development of predictive and diagnostic biomarkers, ultimately benefiting individuals affected by ME/CFS. Materials and methods Study cohort and samples To investigate serum proteome changes in ME/CFS, we utilized 54 baseline ME/CFS patient samples from the two clinical intervention studies RituxME 31 and CycloME 32 , and 27 sex- and age-matched healthy controls, randomly selected from the ME/CFS biobank at Haukeland University Hospital. Characteristics of the study cohort are provided in Table 1 . The included healthy controls and 40 of the ME/CFS patients were non-fasting at the time of sample collection, and these patient had previously been assigned to three subgroups based on their metabolic phenotypes (metabotype 1, 2, and 3) 17 . To facilitate evaluation of the impact of fasting, we included 10 additional patients who had performed overnight fasting before the sampling. In the validation measurements towards the end, samples were randomly picked from the ME/CFS biobank, including up to 212 ME/CFS patients and 66 healthy individuals. Clinical blood and serum analyses were performed according to standard laboratory routines at the hospital. Data processing and analysis Serum samples were analyzed using the SomaScan platform (SomaLogic Operating Co., Inc.), yielding a dataset for 7326 specific aptamer-detected protein targets. Outlier detection removed four samples based on Mahalanobis distances of log10-transformed values, followed by PCA and a chi-square test (p < 0.1). High-leverage aptamers were identified using Z-scores (±1.8, corresponding to the 2.5th and 97.5th percentiles) and assigned NA, after which missing values were imputed using the variable’s minimum and maximum post-outlier removal. Given these adjustments, log10-transformed data were back-transformed using base-10 antilogarithms, followed by a final log2 transformation. Multivariate analysis involved PCA, with the first two principal components used for sample projections, and clinical variables correlated with sample coordinates via the envfit function (Vegan v2.4.0). Univariate analysis was performed using linear regression (adjusting for age, sex, BMI, and fasting baseline) with limma 85 . For the purpose of hypothesis generation, we primarily referred to p-statistics to cover a wider spectrum of biological aspects potentially playing a role in ME/CFS. Explained variance was further assessed on variables with statistical significance (p < 0.05) using the function fitExtractVarPartModel in the VariancePartition library (GitHub - DiseaseNeuroGenomics/variancePartition). Semi-partial Pearson correlation coefficients were calculated for relevant subsets, adjusted for the same covariates, were calculated using ppcor 86 , filtered by a standard cutoff (p 0.3) visualized in a clinical-protein network, where protein communities were defined by shared clinical interactions. Functional enrichment was conducted using Gene Ontology 87 with ClusterProfiler (GitHub - YuLab-SMU/clusterProfiler), while ligand-receptor interactions were annotated via CellLinker 88 , and functional/spatial annotations were retrieved from the Human Protein Atlas 46 , 89 . All visualizations were generated using ggplot2, ComplexHeatmap, tidygraph, igraph, ggnet, and ggpubr. Data analysis was conducted in R 90 within the RStudio environment 91 . Targeted immunodetection (ELISA and Luminex) ELISA was performed using Quantikine Ready-to-Use ELISA kits (R&D Systems) for FGF-21 (Cat#: DF2100), C-peptide (Cat#: DICP00), and GDF-15 (Cat#: DG0150). The serum samples were diluted and measured in duplicates according to the manufacturer’s recommendations. Measurements were performed using the Spark microplate reader (Tecan Trading AG, Switzerland). Standard curve generation was conducted in Excel or GraphPad Prism, with further data analysis done in GraphPad Prism. Custom multiplex assays covering a total of 77 targets (SupplData9) were designed using the Luminex Assay Customization tool on the supplier’s website (Cat# LXSAMH, R&D Systems) and performed according to the manufacturer’s recommendations. Five custom assays were designed to cover the analytical targets, and two 96-well plates for each assay were used. The majority of the samples were analyzed in single reaction wells; however, some samples were analyzed as duplicates or included on multiple plates for quality control. Each plate included fresh target standard dilutions in duplicates. The serum samples were diluted according to the protocol provided by the manufacturer. Plates with identical assays were run on the same day. Measurements were performed using the Luminex 200 instrument (Luminex Corp.) and the accompanying analysis software to generate the standard curves and sample concentrations. Measurements that were beyond the range of the standard curve were excluded, and analytes that displayed an overweight of such measurements were also excluded from further analysis. Data Availability All data produced in the present work are contained as supplementary files to the manuscript Resource availability Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Karl Johan Tronstad ( karl.tronstad{at}uib.no ) Materials availability This study did not generate new unique reagents. Data and code availability Analyzed proteomics data provided by the Somalogic is included as supplemental data. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. This paper does not generate original code. Author contributions AH, FH, HC, ØF, OM, and KJT designed the analytical approach. ØF, OM, IGR, and KS included patients in clinical studies and provided biobank samples. KR, KA, FH, SD, and AH performed laboratory measurements. KAB and HPM provided scientific and technical advice. AH, FH, HC, KJT, and ØF conducted data analyses. AH, FH, HC, and KJT wrote the first version of manuscript. All authors approved the final version of the manuscript. Declaration of interests The authors have declared that no conflict of interest exists. Declaration of generative AI and AI-assisted technologies During the preparation of this manuscript, the author(s) used Microsoft Copilot (UiB institutional license) to assist with translation and ensure readability in certain sections. Supplemental information DocumentS1: Table S1 and Figure S1 SupplData1: Excel file containing log2-transformed aptamer data, and subject metadata. SupplData2: Excel file containing univariate statistics, and cellular compartment metadata, comparing the ME/CFS group and the three metabotype subgroups, with the HC-group. SupplData3: Excel file containing PCA protein loading values, showing influence on PC1 and PC2. SupplData4: Excel file containing the percentage of variance explained by covariates. SupplData5: Excel file containing semi-partial Pearson correlation analysis between proteins and ME/CFS diagnosis, metabotype and physical function level (SF-36PF) SupplData6: Excel file containing univariate statistics for proteins in the MitoCarta 3.0 library. SupplData7: Excel file containing univariate statistics for tissue- and immune cell-specific proteins, including respective protein metadata. SupplData8: Excel file containing univariate statistics for the secretome, including protein function metadata; comparing the ME/CFS group and the three metabotype subgroups, with the HC-group. SupplData9: Excel file containing univariate statistics for the Luminex analysis; comparing the ME/CFS group and the three metabotype subgroups, with the HC-group. Acknowledgement The authors thank the study personnel at each center of the “RituxME” and “CycloME” clinical trials for their efforts in patient follow-up and biobank sampling. This work received financial support from The Kavli Trust, the Research Council of Norway (KJT, projects 272680 and 343168), the Western Norway Regional Health Authority (Helse Vest) and the University of Bergen (PhD grants to FH and AH). The clinical trials that recruited patients for the biobank and the laboratory analyses in this study were financially supported by the Research Council of Norway, the Norwegian Regional Health Trusts, the MEandYou Foundation, the Norwegian ME Association, and the legacy of Torstein Hereid. Additionally, we are grateful for the contributions from private donors and the support from the Norwegian ME Association (Norges ME-forening). References 1. ↵ Jason , L.A. , Yoo , S. , and Bhatia , S . ( 2021 ). Patient perceptions of infectious illnesses preceding Myalgic Encephalomyelitis/Chronic Fatigue Syndrome . Chronic Illness , 174239532110431 . doi: 10.1177/17423953211043106 . OpenUrl CrossRef PubMed 2. Rasa , S. , Nora-Krukle , Z. , Henning , N. , Eliassen , E. , Shikova , E. , Harrer , T. , Scheibenbogen , C. , Murovska , M. , Prusty , B.K. , and European Network on, M.C . ( 2018 ). Chronic viral infections in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) . Journal of translational medicine 16 , 268 . doi: 10.1186/s12967-018-1644-y . OpenUrl CrossRef 3. ↵ Davis , H.E. , McCorkell , L. , Vogel , J.M. , and Topol , E.J . ( 2023 ). Long COVID: major findings, mechanisms and recommendations . Nature Reviews Microbiology . doi: 10.1038/s41579-022-00846-2 . OpenUrl CrossRef PubMed 4. ↵ Carruthers , B.M. , Van De Sande , M.I. , De Meirleir , K.L. , Klimas , N.G. , Broderick , G. , Mitchell , T. , Staines , D. , Powles , A.C.P. , Speight , N. , Vallings , R ., et al. ( 2011 ). Myalgic encephalomyelitis: International Consensus Criteria . Journal of Internal Medicine 270 , 327 – 338 . doi: 10.1111/j.1365-2796.2011.02428.x . OpenUrl CrossRef PubMed 5. ↵ Jason , L.A. , Katz , B.Z. , Sunnquist , M. , Torres , C. , Cotler , J. , and Bhatia , S . ( 2020 ). The Prevalence of Pediatric Myalgic Encephalomyelitis/Chronic Fatigue Syndrome in a Community-Based Sample . Child & Youth Care Forum . doi: 10.1007/s10566-019-09543-3 . OpenUrl CrossRef 6. Nacul , L.C. , Lacerda , E.M. , Pheby , D. , Campion , P. , Molokhia , M. , Fayyaz , S. , Leite , J.C. , Poland , F. , Howe , A. , and Drachler , M.L . ( 2011 ). Prevalence of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) in three regions of England: a repeated cross-sectional study in primary care . BMC Medicine 9 , 91 . doi: 10.1186/1741-7015-9-91 . OpenUrl CrossRef PubMed 7. ↵ Valdez , A.R. , Hancock , E.E. , Adebayo , S. , Kiernicki , D.J. , Proskauer , D. , Attewell , J.R. , Bateman , L. , Demaria , A. , Lapp , C.W. , Rowe , P.C. , and Proskauer , C . ( 2019 ). Estimating Prevalence, Demographics, and Costs of ME/CFS Using Large Scale Medical Claims Data and Machine Learning . Frontiers in Pediatrics 6 . doi: 10.3389/fped.2018.00412 . OpenUrl CrossRef PubMed 8. ↵ Vernon , S.D. , Zheng , T. , Do , H. , Marconi , V.C. , Jason , L.A. , Singer , N.G. , Natelson , B.H. , Sherif , Z.A. , Bonilla , H.F. , Taylor , E. , et al. ( 2025 ). Incidence and Prevalence of Post-COVID-19 Myalgic Encephalomyelitis: A Report from the Observational RECOVER-Adult Study . Journal of General Internal Medicine . doi: 10.1007/s11606-024-09290-9 . OpenUrl CrossRef 9. ↵ Rowe , P.C. , Fontaine , K.R. , Lauver , M. , Jasion , S.E. , Marden , C.L. , Moni , M. , Thompson , C.B. , and Violand , R.L . ( 2016 ). Neuromuscular Strain Increases Symptom Intensity in Chronic Fatigue Syndrome . PloS one 11 , e0159386 . doi: 10.1371/journal.pone.0159386 . OpenUrl CrossRef PubMed 10. ↵ Carruthers , B.M. , Jain , A.K. , De Meirleir , K.L. , Peterson , D.L. , Klimas , N.G. , Lerner , A.M. , Bested , A.C. , Flor-Henry , P. , Joshi , P. , Powles , A.C.P. , et al. ( 2003 ). Myalgic Encephalomyelitis/Chronic Fatigue Syndrome . Journal of Chronic Fatigue Syndrome 11 , 7 – 115 . doi: 10.1300/j092v11n01_02 . OpenUrl CrossRef 11. ↵ Falk Hvidberg , M. , Brinth , L.S. , Olesen , A.V. , Petersen , K.D. , and Ehlers , L. ( 2015 ). The Health-Related Quality of Life for Patients with Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) . PloS one 10 , e0132421 . doi: 10.1371/journal.pone.0132421 . OpenUrl CrossRef PubMed 12. Pheby , D.F.H. , Araja , D. , Berkis , U. , Brenna , E. , Cullinan , J. , De Korwin , J.-D. , Gitto , L. , Hughes , D.A. , Hunter , R.M. , Trepel , D. , and Wang-Steverding , X . ( 2020 ). The Development of a Consistent Europe-Wide Approach to Investigating the Economic Impact of Myalgic Encephalomyelitis (ME/CFS): A Report from the European Network on ME/CFS (EUROMENE) . Healthcare 8 , 88 . doi: 10.3390/healthcare8020088 . OpenUrl CrossRef PubMed 13. ↵ Kielland , A. , and Liu , J . ( 2025 ). What can wage development before and after a G93.3 diagnosis tell us about prognoses for myalgic encephalomyelitis? Social Sciences & Humanities Open 11 ( 101206 ). 14. ↵ Komaroff , A.L . ( 2019 ). Advances in Understanding the Pathophysiology of Chronic Fatigue Syndrome . JAMA 322 , 499 . doi: 10.1001/jama.2019.8312 . OpenUrl CrossRef PubMed 15. ↵ Choutka , J. , Jansari , V. , Hornig , M. , and Iwasaki , A . ( 2022 ). Unexplained post-acute infection syndromes . Nature Medicine 28 , 911 – 923 . doi: 10.1038/s41591-022-01810-6 . OpenUrl CrossRef PubMed 16. ↵ Fluge , Ø. , Tronstad , K.J. , and Mella , O . ( 2021 ). Pathomechanisms and possible interventions in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) . Journal of Clinical Investigation 131 . doi: 10.1172/jci150377 . OpenUrl CrossRef 17. ↵ Hoel , F. , Hoel , A. , Pettersen , I.K.N. , Rekeland , I.G. , Risa , K. , Alme , K. , Sørland , K. , Fosså , A. , Lien , K. , Herder , I. , et al. ( 2021 ). A map of metabolic phenotypes in patients with myalgic encephalomyelitis/chronic fatigue syndrome . JCI Insight 6 . doi: 10.1172/jci.insight.149217 . OpenUrl CrossRef 18. ↵ Appelman , B. , Charlton , B.T. , Goulding , R.P. , Kerkhoff , T.J. , Breedveld , E.A. , Noort , W. , Offringa , C. , Bloemers , F.W. , Van Weeghel , M. , Schomakers , B.V ., et al. ( 2024 ). Muscle abnormalities worsen after post-exertional malaise in long COVID . Nature communications 15 . doi: 10.1038/s41467-023-44432-3 . OpenUrl CrossRef PubMed 19. ↵ Uhlen , M. , Fagerberg , L. , Hallstrom , B.M. , Lindskog , C. , Oksvold , P. , Mardinoglu , A. , Sivertsson , A. , Kampf , C. , Sjostedt , E. , Asplund , A. , et al. ( 2015 ). Proteomics. Tissue-based map of the human proteome . Science 347 , 1260419 . doi: 10.1126/science.1260419 . OpenUrl Abstract / FREE Full Text 20. ↵ Jiang , L. , Wang , M. , Lin , S. , Jian , R. , Li , X. , Chan , J. , Dong , G. , Fang , H. , Robinson , A.E. , Snyder , M.P. , et al. ( 2020 ). A Quantitative Proteome Map of the Human Body . Cell 183 , 269 – 283.e219 . doi: 10.1016/j.cell.2020.08.036 . OpenUrl CrossRef PubMed 21. ↵ Anderson , N.L. , and Anderson , N.G . ( 2002 ). The Human Plasma Proteome . Molecular & Cellular Proteomics 1 , 845 – 867 . doi: 10.1074/mcp.r200007-mcp200 . OpenUrl CrossRef PubMed 22. ↵ Hornig , M. , Montoya , J.G. , Klimas , N.G. , Levine , S. , Felsenstein , D. , Bateman , L. , Peterson , D.L. , Gottschalk , C.G. , Schultz , A.F. , Che , X. , et al. ( 2015 ). Distinct plasma immune signatures in ME/CFS are present early in the course of illness . Sci Adv 1 . doi: 10.1126/sciadv.1400121 . OpenUrl FREE Full Text 23. ↵ Lunde , S. , Kristoffersen , E.K. , Sapkota , D. , Risa , K. , Dahl , O. , Bruland , O. , Mella , O. , and Fluge , Ø . ( 2016 ). Serum BAFF and APRIL Levels, T-Lymphocyte Subsets, and Immunoglobulins after B-Cell Depletion Using the Monoclonal Anti-CD20 Antibody Rituximab in Myalgic Encephalopathy/Chronic Fatigue Syndrome . PloS one 11 , e0161226 . doi: 10.1371/journal.pone.0161226 . OpenUrl CrossRef PubMed 24. ↵ Cambras , T. , Zerón-Rugerio , M.F. , Díez-Noguera , A. , Zaragozá , M.C. , Domingo , J.C. , Sanmartin-Sentañes , R. , Alegre-Martin , J. , and Castro-Marrero , J . ( 2023 ). Skin Temperature Circadian Rhythms and Dysautonomia in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: The Role of Endothelin-1 in the Vascular Tone Dysregulation . International journal of molecular sciences 24 , 4835 . doi: 10.3390/ijms24054835 . OpenUrl CrossRef 25. ↵ Baklund , I.H. , Dammen , T. , Moum , T.Å. , Kristiansen , W. , Duarte , D.S. , Castro-Marrero , J. , Helland , I.B. , and Strand , E.B . ( 2021 ). Evaluating Routine Blood Tests According to Clinical Symptoms and Diagnostic Criteria in Individuals with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome . Journal of Clinical Medicine 10 , 3105 . doi: 10.3390/jcm10143105 . OpenUrl CrossRef 26. ↵ Gold , L. , Ayers , D. , Bertino , J. , Bock , C. , Bock , A. , Brody , E.N. , Carter , J. , Dalby , A.B. , Eaton , B.E. , Fitzwater , T. , et al. ( 2010 ). Aptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery . PloS one 5 , e15004 . doi: 10.1371/journal.pone.0015004 . OpenUrl CrossRef PubMed 27. ↵ Lehallier , B. , Gate , D. , Schaum , N. , Nanasi , T. , Lee , S.E. , Yousef , H. , Moran Losada , P. , Berdnik , D. , Keller , A. , Verghese , J. , et al. ( 2019 ). Undulating changes in human plasma proteome profiles across the lifespan . Nature Medicine 25 , 1843 – 1850 . doi: 10.1038/s41591-019-0673-2 . OpenUrl CrossRef PubMed 28. ↵ Niewczas , M.A. , Pavkov , M.E. , Skupien , J. , Smiles , A. , Md Dom , Z.I. , Wilson , J.M. , Park , J. , Nair , V. , Schlafly , A. , Saulnier , P.-J. , et al. ( 2019 ). A signature of circulating inflammatory proteins and development of end-stage renal disease in diabetes . Nature Medicine 25 , 805 – 813 . doi: 10.1038/s41591-019-0415-5 . OpenUrl CrossRef PubMed 29. ↵ Germain , A. , Levine , S.M. , and Hanson , M.R . ( 2021 ). In-Depth Analysis of the Plasma Proteome in ME/CFS Exposes Disrupted Ephrin-Eph and Immune System Signaling . Proteomes 9 , 6 . doi: 10.3390/proteomes9010006 . OpenUrl CrossRef 30. ↵ Walitt , B. , Singh , K. , Lamunion , S.R. , Hallett , M. , Jacobson , S. , Chen , K. , Enose-Akahata , Y. , Apps , R. , Barb , J.J. , Bedard , P. , et al. ( 2024 ). Deep phenotyping of post-infectious myalgic encephalomyelitis/chronic fatigue syndrome . Nature communications 15 . doi: 10.1038/s41467-024-45107-3 . OpenUrl CrossRef PubMed 31. ↵ Fluge , Ø. , Rekeland , I.G. , Lien , K. , Thürmer , H. , Borchgrevink , P.C. , Schäfer , C. , Sørland , K. , Aßmus , J ., Ktoridou-Valen , I. , Herder , I ., et al. ( 2019 ). B-Lymphocyte Depletion in Patients With Myalgic Encephalomyelitis/Chronic Fatigue Syndrome . Annals of Internal Medicine 170 , 585 . doi: 10.7326/m18-1451 . OpenUrl CrossRef 32. ↵ Rekeland , I.G. , Fosså , A. , Lande , A. , Ktoridou-Valen , I. , Sørland , K. , Holsen , M. , Tronstad , K.J. , Risa , K. , Alme , K. , Viken , M.K. , et al. ( 2020 ). Intravenous Cyclophosphamide in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. An Open-Label Phase II Study . Frontiers in Medicine 7 . doi: 10.3389/fmed.2020.00162 . OpenUrl CrossRef 33. ↵ Petretto , A. , Bruschi , M. , Pratesi , F. , Croia , C. , Candiano , G. , Ghiggeri , G. , and Migliorini , P . ( 2019 ). Neutrophil extracellular traps (NET) induced by different stimuli: A comparative proteomic analysis . PloS one 14 , e0218946 . doi: 10.1371/journal.pone.0218946 . OpenUrl CrossRef PubMed 34. ↵ Päll , T. , Pink , A. , Kasak , L. , Turkina , M. , Anderson , W. , Valkna , A. , and Kogerman , P . ( 2011 ). Soluble CD44 Interacts with Intermediate Filament Protein Vimentin on Endothelial Cell Surface . PloS one 6 , e29305 . doi: 10.1371/journal.pone.0029305 . OpenUrl CrossRef PubMed 35. ↵ Cryan , L.M. , Tsang , T.-M. , Stiles , J. , Bazinet , L. , Lee , S.L. , Garrard , S. , Madrian , E. , Roberts , C. , Payne , J. , Jensen , A. , et al. ( 2022 ). Capillary morphogenesis gene 2 (CMG2) mediates growth factor-induced angiogenesis by regulating endothelial cell chemotaxis . Angiogenesis 25 , 397 – 410 . doi: 10.1007/s10456-022-09833-w . OpenUrl CrossRef PubMed 36. ↵ Chung , J. , Kim , K.H. , An , S.H. , Lee , S. , Lim , B.-K. , Kang , S.W. , and Kwon , K . ( 2019 ). Coxsackievirus and adenovirus receptor mediates the responses of endothelial cells to fluid shear stress . Experimental & Molecular Medicine 51 , 1 – 15 . doi: 10.1038/s12276-019-0347-7 . OpenUrl CrossRef 37. ↵ Gatto , C. , Grandi , M. , Solaini , G. , Baracca , A. , and Giorgio , V . ( 2022 ). The F1Fo-ATPase inhibitor protein IF1 in pathophysiology . Front Physiol 13 , 917203 . doi: 10.3389/fphys.2022.917203 . OpenUrl CrossRef PubMed 38. ↵ Chen , X. , Wang , Y. , Li , Y. , Liu , G. , Liao , K. , and Song , F . ( 2022 ). Identification of immune-related cells and genes in the breast invasive carcinoma microenvironment . Aging 14 , 1374 – 1388 . doi: 10.18632/aging.203879 . OpenUrl CrossRef PubMed 39. ↵ Qi , P. , Huang , M. , and Li , T . ( 2022 ). Screening the Potential Biomarkers of COVID-19-Related Thrombosis Through Bioinformatics Analysis . Front Genet 13 , 889348 . doi: 10.3389/fgene.2022.889348 . OpenUrl CrossRef 40. ↵ Hotamisligil , G.S. , and Bernlohr , D.A . ( 2015 ). Metabolic functions of FABPs—mechanisms and therapeutic implications . Nature Reviews Endocrinology 11 , 592 – 605 . doi: 10.1038/nrendo.2015.122 . OpenUrl CrossRef PubMed 41. ↵ Ernst , M.C. , and Sinal , C.J . ( 2010 ). Chemerin: at the crossroads of inflammation and obesity . Trends Endocrinol Metab 21 , 660 – 667 . doi: 10.1016/j.tem.2010.08.001 . OpenUrl CrossRef PubMed 42. ↵ Abudureyimu , S. , Abulaiti , P. , Li , H. , Xing , Z. , Liu , S. , Li , W. , and Gao , Y . ( 2021 ). Roles of endothelial lipase gene related single nucleotide polymorphisms in patients with coronary artery disease . Gene 788 , 145669 . doi: 10.1016/j.gene.2021.145669 . OpenUrl CrossRef 43. ↵ Meissner , F. , Scheltema , R.A. , Mollenkopf , H.-J. , and Mann , M . ( 2013 ). Direct Proteomic Quantification of the Secretome of Activated Immune Cells . Science 340 , 475 – 478 . doi: 10.1126/science.1232578 . OpenUrl Abstract / FREE Full Text 44. ↵ Rath , S. , Sharma , R. , Gupta , R. , Ast , T. , Chan , C. , Durham , T.J. , Goodman , R.P. , Grabarek , Z. , Haas , M.E. , Hung , W.H.W. , et al. ( 2021 ). MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations . Nucleic acids research 49 , D1541 – D1547 . doi: 10.1093/nar/gkaa1011 . OpenUrl CrossRef PubMed 45. ↵ Lominadze , G. , Powell , D.W. , Luerman , G.C. , Link , A.J. , Ward , R.A. , and McLeish , K.R . ( 2005 ). Proteomic Analysis of Human Neutrophil Granules . Molecular & Cellular Proteomics 4 , 1503 – 1521 . doi: 10.1074/mcp.m500143-mcp200 . OpenUrl CrossRef PubMed 46. ↵ Uhlen , M. , Karlsson , M.J. , Hober , A. , Svensson , A.S. , Scheffel , J. , Kotol , D. , Zhong , W. , Tebani , A. , Strandberg , L. , Edfors , F. , et al. ( 2019 ). The human secretome . Sci Signal 12 . doi: 10.1126/scisignal.aaz0274 . OpenUrl Abstract / FREE Full Text 47. ↵ Campen , C.M.C.V. , and Visser , F.C . ( 2018 ). Blood Volume Status in Patients with Chronic Fatigue Syndrome: Relation to Complaints . International Journal of Clinical Medicine 09 , 809 – 819 . doi: 10.4236/ijcm.2018.911067 . OpenUrl CrossRef 48. ↵ Petersen , S.G. , Saxne , T. , Heinegard , D. , Hansen , M. , Holm , L. , Koskinen , S. , Stordal , C. , Christensen , H. , Aagaard , P. , and Kjaer , M . ( 2010 ). Glucosamine but not ibuprofen alters cartilage turnover in osteoarthritis patients in response to physical training . Osteoarthritis and Cartilage 18 , 34 – 40 . doi: 10.1016/j.joca.2009.07.004 . OpenUrl CrossRef PubMed Web of Science 49. ↵ Jammes , Y. , Adjriou , N. , Kipson , N. , Criado , C. , Charpin , C. , Rebaudet , S. , Stavris , C. , Guieu , R. , Fenouillet , E. , and Retornaz , F . ( 2020 ). Altered muscle membrane potential and redox status differentiates two subgroups of patients with chronic fatigue syndrome . Journal of translational medicine 18 . doi: 10.1186/s12967-020-02341-9 . OpenUrl CrossRef 50. Pietrangelo , T. , Fulle , S. , Coscia , F. , Gigliotti , P.V. , and Fanò-Illic , G . ( 2018 ). Old muscle in young body: an aphorism describing the Chronic Fatigue Syndrome . European Journal of Translational Myology 28 . doi: 10.4081/ejtm.2018.7688 . OpenUrl CrossRef 51. ↵ Petter , E. , Scheibenbogen , C. , Linz , P. , Stehning , C. , Wirth , K. , Kuehne , T. , and Kelm , M . ( 2022 ). Muscle sodium content in patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome . Journal of translational medicine 20 . doi: 10.1186/s12967-022-03616-z . OpenUrl CrossRef PubMed 52. ↵ Montoya , J.G. , Holmes , T.H. , Anderson , J.N. , Maecker , H.T. , Rosenberg-Hasson , Y. , Valencia , I.J. , Chu , L. , Younger , J.W. , Tato , C.M. , and Davis , M.M . ( 2017 ). Cytokine signature associated with disease severity in chronic fatigue syndrome patients . Proceedings of the National Academy of Sciences 114 , E7150 – E7158 . doi: 10.1073/pnas.1710519114 . OpenUrl Abstract / FREE Full Text 53. ↵ Landi , A. , Broadhurst , D. , Vernon , S.D. , Tyrrell , D.L. , and Houghton , M . ( 2016 ). Reductions in circulating levels of IL-16, IL-7 and VEGF-A in myalgic encephalomyelitis/chronic fatigue syndrome . Cytokine 78 , 27 – 36 . doi: 10.1016/j.cyto.2015.11.018 . OpenUrl CrossRef PubMed 54. ↵ Ishimura , S. , Furuhashi , M. , Watanabe , Y. , Hoshina , K. , Fuseya , T. , Mita , T. , Okazaki , Y. , Koyama , M. , Tanaka , M. , Akasaka , H. , et al. ( 2013 ). Circulating Levels of Fatty Acid-Binding Protein Family and Metabolic Phenotype in the General Population . PloS one 8 , e81318 . doi: 10.1371/journal.pone.0081318 . OpenUrl CrossRef PubMed 55. ↵ Iu , D.S. , Maya , J. , Vu , L.T. , Fogarty , E.A. , McNairn , A.J. , Ahmed , F. , Franconi , C.J. , Munn , P.R. , Grenier , J.K. , Hanson , M.R. , and Grimson , A . ( 2024 ). Transcriptional reprogramming primes CD8+ T cells toward exhaustion in Myalgic encephalomyelitis/chronic fatigue syndrome . Proceedings of the National Academy of Sciences 121 . doi: 10.1073/pnas.2415119121 . OpenUrl CrossRef 56. ↵ Iwasaki , A. , and Putrino , D . ( 2023 ). Why we need a deeper understanding of the pathophysiology of long COVID . The Lancet Infectious Diseases . doi: 10.1016/s1473-3099(23)00053-1 . OpenUrl CrossRef PubMed 57. ↵ Nunes , J. , Kruger , A. , Proal , A. , Kell , D. , and Pretorius , E . ( 2022 ). The Occurrence of Hyperactivated Platelets and Fibrinaloid Microclots in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) . Pharmaceuticals 15 , 931 . doi: 10.3390/ph15080931 . OpenUrl CrossRef PubMed 58. ↵ Vu , L.T. , Ahmed , F. , Zhu , H. , Iu , D.S.H. , Fogarty , E.A. , Kwak , Y. , Chen , W. , Franconi , C.J. , Munn , P.R. , Tate , A.E. , et al. ( 2024 ). Single-cell transcriptomics of the immune system in ME/CFS at baseline and following symptom provocation . Cell Reports Medicine 5 , 101373 . doi: 10.1016/j.xcrm.2023.101373 . OpenUrl CrossRef 59. ↵ Cassatella , M.A. , Östberg , N.K. , Tamassia , N. , and Soehnlein , O . ( 2019 ). Biological Roles of Neutrophil-Derived Granule Proteins and Cytokines . Trends in Immunology 40 , 648 – 664 . doi: 10.1016/j.it.2019.05.003 . OpenUrl CrossRef PubMed 60. ↵ Koenderman , L. , Tesselaar , K. , and Vrisekoop , N . ( 2022 ). Human neutrophil kinetics: a call to revisit old evidence . Trends Immunol 43 , 868 – 876 . doi: 10.1016/j.it.2022.09.008 . OpenUrl CrossRef PubMed 61. ↵ Aymonnier , K. , Amsler , J. , Lamprecht , P. , Salama , A. , and Witko-Sarsat , V . ( 2022 ). The neutrophil: A key resourceful agent in immune-mediated vasculitis . Immunological Reviews . doi: 10.1111/imr.13170 . OpenUrl CrossRef PubMed 62. ↵ Candia , J. , Fantoni , G. , Delgado-Peraza , F. , Shehadeh , N. , Tanaka , T. , Moaddel , R. , Walker , K.A. , and Ferrucci , L . ( 2024 ). Variability of 7K and 11K SomaScan Plasma Proteomics Assays . J Proteome Res 23 , 5531 – 5539 . doi: 10.1021/acs.jproteome.4c00667 . OpenUrl CrossRef PubMed 63. ↵ Fluge , O. , Mella , O. , Bruland , O. , Risa , K. , Dyrstad , S.E. , Alme , K. , Rekeland , I.G. , Sapkota , D. , Rosland , G.V. , Fossa , A. , et al. ( 2016 ). Metabolic profiling indicates impaired pyruvate dehydrogenase function in myalgic encephalopathy/chronic fatigue syndrome . JCI Insight 1 , e89376 . doi: 10.1172/jci.insight.89376 . OpenUrl CrossRef 64. ↵ Lien , K. , Johansen , B. , Veierød , M.B. , Haslestad , A.S. , Bøhn , S.K. , Melsom , M.N. , Kardel , K.R. , and Iversen , P.O . ( 2019 ). Abnormal blood lactate accumulation during repeated exercise testing in myalgic encephalomyelitis/chronic fatigue syndrome . Physiological reports 7 , e14138 . doi: 10.14814/phy2.14138 . OpenUrl CrossRef PubMed 65. ↵ Nalbant , D. , Youn , H. , Nalbant , S.I. , Sharma , S. , Cobos , E. , Beale , E.G. , Du , Y. , and Williams , S.C . ( 2005 ). FAM20: an evolutionarily conserved family of secreted proteins expressed in hematopoietic cells . BMC genomics 6 . doi: 10.1186/1471-2164-6-11 . OpenUrl CrossRef PubMed 66. ↵ Sandvik , M.K. , Sørland , K. , Leirgul , E. , Rekeland , I.G. , Stavland , C.S. , Mella , O. , and Fluge , Ø . ( 2023 ). Endothelial dysfunction in ME/CFS patients . PloS one 18 , e0280942 . doi: 10.1371/journal.pone.0280942 . OpenUrl CrossRef PubMed 67. Sørland , K. , Sandvik , M.K. , Rekeland , I.G. , Ribu , L. , Småstuen , M.C. , Mella , O. , and Fluge , Ø . ( 2021 ). Reduced Endothelial Function in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome– Results From Open-Label Cyclophosphamide Intervention Study . Frontiers in Medicine 8 . doi: 10.3389/fmed.2021.642710 . OpenUrl CrossRef 68. ↵ Scherbakov , N. , Szklarski , M. , Hartwig , J. , Sotzny , F. , Lorenz , S. , Meyer , A. , Grabowski , P. , Doehner , W. , and Scheibenbogen , C . ( 2020 ). Peripheral endothelial dysfunction in myalgic encephalomyelitis/chronic fatigue syndrome . ESC Heart Failure 7 , 1064 – 1071 . doi: 10.1002/ehf2.12633 . OpenUrl CrossRef PubMed 69. ↵ Feletou , M . ( 2011 ). In: The Endothelium: Part 1: Multiple Functions of the Endothelial Cells-Focus on Endothelium-Derived Vasoactive Mediators . In The Endothelium: Part 1: Multiple Functions of the Endothelial Cells-Focus on Endothelium-Derived Vasoactive Mediators . doi: 10.4199/C00031ED1V01Y201105ISP019 . OpenUrl CrossRef 70. ↵ Wilson , C. , Lee , M.D. , Buckley , C. , Zhang , X. , and McCarron , J.G . ( 2023 ). Mitochondrial ATP Production is Required for Endothelial Cell Control of Vascular Tone . Function (Oxf ) 4 , zqac063. doi: 10.1093/function/zqac063 . OpenUrl CrossRef 71. ↵ McMahon , T.J . ( 2019 ). Red Blood Cell Deformability, Vasoactive Mediators, and Adhesion . Front Physiol 10 , 1417 . doi: 10.3389/fphys.2019.01417 . OpenUrl CrossRef 72. ↵ Aschman , T. , Wyler , E. , Baum , O. , Hentschel , A. , Legler , F. , Preusse , C. , Meyer-Arndt , L. , Büttnerova , I. , Förster , A. , Cengiz , D. , et al. ( 2023 ). Post-COVID syndrome is associated with capillary alterations, macrophage infiltration and distinct transcriptomic signatures in skeletal muscles . Cold Spring Harbor Laboratory . 73. ↵ Iosef , D.C. , Knauer , M.J. , Nicholson , M. , Nynatten , L.R.V. , Cepinskas , D.G. , Draghici , S. , Han , V.K.M. , and Fraser ( 2023 ). Plasma Proteome of Long-covid Patients Indicates Hypoxia-mediated Vasculo-proliferative Disease With Impact on Brain and Heart Function . Research Square Platform LLC . 74. ↵ Hanson , A.L. , Mulè , M.P. , Ruffieux , H. , Mescia , F. , Bergamaschi , L. , Pelly , V.S. , Turner , L. , Kotagiri , P. , Göttgens , B. , Hess , C. , et al. ( 2024 ). Iron dysregulation and inflammatory stress erythropoiesis associates with long-term outcome of COVID-19 . Nature Immunology 25 , 471 – 482 . doi: 10.1038/s41590-024-01754-8 . OpenUrl CrossRef PubMed 75. ↵ Wang , P.-Y. , Ma , J. , Kim , Y.-C. , Son , A.Y. , Syed , A.M. , Liu , C. , Mori , M.P. , Huffstutler , R.D. , Stolinski , J.L. , Talagala , S.L. , et al. ( 2023 ). WASF3 disrupts mitochondrial respiration and may mediate exercise intolerance in myalgic encephalomyelitis/chronic fatigue syndrome . Proceedings of the National Academy of Sciences 120 . doi: 10.1073/pnas.2302738120 . OpenUrl CrossRef PubMed 76. ↵ Melvin , A. , Lacerda , E. , Dockrell , H.M. , O’Rahilly , S. , and Nacul , L . ( 2019 ). Circulating levels of GDF15 in patients with myalgic encephalomyelitis/chronic fatigue syndrome . Journal of translational medicine 17 . doi: 10.1186/s12967-019-02153-6 . OpenUrl CrossRef PubMed 77. ↵ Chow , L.S. , Gerszten , R.E. , Taylor , J.M. , Pedersen , B.K. , Van Praag , H. , Trappe , S. , Febbraio , M.A. , Galis , Z.S. , Gao , Y. , Haus , J.M. , et al. ( 2022 ). Exerkines in health, resilience and disease . Nature Reviews Endocrinology 18 , 273 – 289 . doi: 10.1038/s41574-022-00641-2 . OpenUrl CrossRef PubMed 78. ↵ Chen , P.-S. , Chiu , W.-T. , Hsu , P.-L. , Lin , S.-C. , Peng , I.C. , Wang , C.-Y. , and Tsai , S.-J . ( 2020 ). Pathophysiological implications of hypoxia in human diseases . Journal of Biomedical Science 27 . doi: 10.1186/s12929-020-00658-7 . OpenUrl CrossRef PubMed 79. ↵ Grassi , B. , Marzorati , M. , Lanfranconi , F. , Ferri , A. , Longaretti , M. , Stucchi , A. , Vago , P. , Marconi , C. , and Morandi , L . ( 2007 ). Impaired oxygen extraction in metabolic myopathies: Detection and quantification by near-infrared spectroscopy . Muscle & Nerve 35 , 510 – 520 . doi: 10.1002/mus.20708 . OpenUrl CrossRef PubMed Web of Science 80. ↵ Joseph , P. , Arevalo , C. , Oliveira , R.K.F. , Faria-Urbina , M. , Felsenstein , D. , Oaklander , A.L. , and Systrom , D.M . ( 2021 ). Insights From Invasive Cardiopulmonary Exercise Testing of Patients With Myalgic Encephalomyelitis/Chronic Fatigue Syndrome . Chest 160 , 642 – 651 . doi: 10.1016/j.chest.2021.01.082 . OpenUrl CrossRef PubMed 81. ↵ Singh , I. , Joseph , P. , Heerdt , P.M. , Cullinan , M. , Lutchmansingh , D.D. , Gulati , M. , Possick , J.D. , Systrom , D.M. , and Waxman , A.B . ( 2022 ). Persistent Exertional Intolerance After COVID-19: Insights From Invasive Cardiopulmonary Exercise Testing . Chest 161 , 54 – 63 . doi: 10.1016/j.chest.2021.08.010 . OpenUrl CrossRef PubMed 82. ↵ Zhang , S. , Jahanbani , F. , Chander , V. , Kjellberg , M. , Liu , M. , Glass , K.A. , Iu , D.S. , Ahmed , F. , Li , H. , Maynard , R.D. , et al. ( 2025 ). Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis . Cold Spring Harbor Laboratory . 83. ↵ Huang , K. , Lidbury , B.A. , Thomas , N. , Gooley , P.R. , and Armstrong , C.W . ( 2025 ). Machine learning and multi-omics in precision medicine for ME/CFS . Journal of translational medicine 23 . doi: 10.1186/s12967-024-05915-z . OpenUrl CrossRef 84. ↵ Chapola , H. , de Bastiani , M.A. , Duarte , M.M. , Freitas , M.B. , Schuster , J.S. , de Vargas , D.M. , and Klamt , F. ( 2023 ). A comparative study of COVID-19 transcriptional signatures between clinical samples and preclinical cell models in the search for disease master regulators and drug repositioning candidates . Virus Res 326 , 199053 . doi: 10.1016/j.virusres.2023.199053 . OpenUrl CrossRef 85. ↵ Ritchie , M.E. , Phipson , B. , Wu , D. , Hu , Y. , Law , C.W. , Shi , W. , and Smyth , G.K . ( 2015 ). limma powers differential expression analyses for RNA-sequencing and microarray studies . Nucleic acids research 43 , e47 – e47 . doi: 10.1093/nar/gkv007 . OpenUrl CrossRef PubMed 86. ↵ Kim , S . ( 2015 ). ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients . Communications for Statistical Applications and Methods 22 , 665 – 674 . doi: 10.5351/csam.2015.22.6.665 . OpenUrl CrossRef 87. ↵ The-Gene-Ontology-Consortium ( 2019 ). The Gene Ontology Resource: 20 years and still GOing strong . Nucleic acids research 47 , D330 – D338 . doi: 10.1093/nar/gky1055 . OpenUrl CrossRef PubMed 88. ↵ Zhang , Y. , Liu , T. , Wang , J. , Zou , B. , Li , L. , Yao , L. , Chen , K. , Ning , L. , Wu , B. , Zhao , X. , and Wang , D . ( 2021 ). Cellinker: a platform of ligand–receptor interactions for intercellular communication analysis . Bioinformatics 37 , 2025 – 2032 . doi: 10.1093/bioinformatics/btab036 . OpenUrl CrossRef 89. ↵ Thul , P.J. , and Lindskog , C . ( 2018 ). The human protein atlas: A spatial map of the human proteome . Protein Science 27 , 233 – 244 . doi: 10.1002/pro.3307 . OpenUrl CrossRef PubMed 90. ↵ R-Core-Team ( 2019 ). R: A Language and Environment for Statistical Computing ( R Foundation for Statistical Computing , Vienna, Austria ). 91. ↵ RStudio-Team ( 2015 ). RStudio: Integrated Development for R. RStudio , Inc., Boston, MA . View the discussion thread. Back to top Previous Next Posted May 31, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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