Subject characteristics
All QFS and CFS patients were severely fatigued and functionally impaired at the time of sample collection. Mean fatigue severity scores were significantly higher for CFS patients compared to QFS patients (Student’s t test, P = 0.0034). No significant differences in mean functional impairment scores were observed when comparing QFS patients with CFS patients (Student’s t test, P = 0.3055) (Table 1).
QFS and CFS show expression profiles of inflammatory proteins distinct from HC
Differential expression of circulating inflammatory proteins is shown in Table 2. In total, there are 5, 27, and 0 proteins identified to be differentially expressed (FDR < 0.05) when comparing QFS to HC, CFS to HC, and QFS to CFS, respectively (Figure 1 and Supplementary figure 5)..
Inflammatory proteomics-based models can discriminate QFS, CFS and HC
Supplementary figure 1 depicts the varying prediction performance of the model with different partition of data by cross validation. The median of training and prediction performance in QFS versus HC and CFS versus HC is close to 1, while the median of training and prediction performance in QFS versus CFS is lower. Based on the large difference of protein expression levels, it is relatively easier to discriminate QFS and CFS from HC. The following variables proved most important in prediction when comparing QFS with HC; 4E-BP1, CD40, AXIN1, CCL11, CD244, IL-8, OPG, CCL4, TRAIL, and CD8A, CFS with HC; 4E-BP1, CDCP1, AXIN1, MMP-10, CSF-1, TNFB, NT-3, FGF-23, IL-12B, and IL-8, and QFS and CFS with HC; 4E-BP1, AXIN1, CD40, CDCP1, CSF-1, IL-8, FGF-23, CCL4, ADA, and MMP-10 (Supplementary figure 2).
Differential association patterns between inflammatory protein and metabolites in disease and health
There are 319, 441, and 12 significantly in- and decreased metabolites when comparing QFS patients to HC, CFS patients to HC, and QFS patients to CFS patients, respectively (FDR < 0.05, Figure 2, Supplementary figure 5, and Supplementary table 1). When comparing QFS to HC, the identified metabolites are enriched in primary bile acid biosynthesis (P = 0.0116), sphingolipid metabolism (P = 0.0256), nitrogen metabolism (P = 0.0394), and D-glutamine and D-glutamate metabolism (P = 0.0394) pathways. When comparing CFS to HC, the sphingolipid metabolism (P = 0.0033) pathway is enriched. When comparing QFS patients to CFS patients, the nitrogen (P = 0.0154), D-Glutamine and D-Glutamate metabolism (P = 0.0154), arginine (P = 0.0357), butanoate (P = 0.387), and histidine metabolism (P = 0.0407) are enriched.
Next, we investigated in which way the inflammatory proteins are associated with the metabolites in patients and healthy individuals, respectively. We illustrate the correlation between the differentially expressed proteins (FDR < 0.05) and the top 20 differentially expressed metabolites (with similar number of proteins) in QFS + CFS patients versus HC. This clustering pattern was then used as a reference for the same type of data from a population-based cohort of 318 individuals (www.humanfunctionalgenomics.org) (Figure 3). As shown in Figure 3, metabolites acetohexamide, sphingosine 1-phosphate, l-cysteinylglycine, l-cysteine, and 2-(2,4-dihydroxy-5-m are of particular interest as they positively correlate with inflammatory proteins. Validation experiments with PBMCs of HC showed that stimulation with 25 mM l-cysteinylglycine resulted in a significantly higher MCP-1 production compared to RPMI as a negative control (Mann-Whitney U test, P = 0.0238). No significant differences were observed for TGF-β, or MCP-1 when stimulating with lower concentrations, i.e., 0.5 mM and 5 mM, of l-cysteinylglycine, l-cysteine, or acetohexamide (Figure 4).
QFS and CFS show a microbiome composition distinct from HC
A PCoA on gut microbiome taxonomy of QFS, CFS, and HC was performed, showing a clear-cut difference between QFS and CFS, and HC (Figure 5). There are 36, 44, and 2 features showing significant differences in gut microbiome taxonomy when comparing QFS to HC, CFS to HC, and QFS to CFS, respectively (Supplementary figure 3, Supplementary figure 5, and Supplementary table 2). When comparing QFS patients to HC there is an increase in abundance of Bacteroidetes with Bacteroides and Alistiples spp., and a decrease in abundance of Firmicutes and Actinobacteria with Ruminococcus and Bifidobacterium spp., respectively. When comparing CFS patients to HC, we find an increase in abundance of Firmicutes and Actinobacteria with Ruminococcus and Bifidobacterium spp., respectively, and a decrease in abundance of Bacteroidetes with Alistiples and Bacteroides spp. When comparing QFS patients to CFS patients, we find a slight increase in abundance of Firmicutes with Eubacterium and Faecalibacterium spp. in the former. Supplementary table 3 depicts significantly in- and decreased gut microbiome functional pathways when comparing QFS to HC, CFS to HC, and QFS to CFS.
Next, we investigated in which way the gut microbiome is associated with metabolites in fatigued patients, i.e., QFS and CFS, as HC hardly show any overlap. Only two significant correlations were found; Bifidobacterium_adolescentis and N-Docosahexaenoyl GABA, and Subdoligranulum_unclassified and Arbekacin (Supplementary figure 4).