Study population characteristics

Subjects with ME/CFS were established patients of a ME/CFS specialist, Susan Levine, M.D. and fit the Fukuda diagnostic criteria [1]. This study began before the criteria for systemic exertion intolerance disease (SEID) were established [22], but most, perhaps all, also fit the description of SEID. Of the 48 patients and 39 control participants who self-reported good health, 34 ME/CFS patients and 7 controls self-reported gastrointestinal disturbances such as constipation, diarrhea, or intestinal discomfort. Many ME/CFS patients are able to identify an acute, often flu-like, illness that immediately preceded the onset of the disease eventually diagnosed as ME/CFS, while others are unaware of an initiating event and consider their onset to be gradual. Among the 48 ME/CFS patients in the study, 19 indicated a gradual and 25 stated a sudden onset. ME/CFS subjects completed the SF-36 form (Additional file 1: Figure S1) and Bell’s Disability scale (Table 1).

Table 1 Characteristics of the study population Full size table

In comparison to other studies in which patients diagnosed with ME/CFS also filled out the SF-36 form, our study population fell within the same ranges on the eight subscales of the SF-36 (Additional file 1: Figure S1).

Measurements of levels of microbial translocation markers indicate microbial translocation

We quantified plasma levels of hsCRP, lipopolysaccharides (LPS) as a marker of microbial translocation (MT) and plasma intestinal fatty acid binding protein (I-FABP) as a marker for gastrointestinal tract damage in both groups. The distribution of plasma hsCRP, LPS and I-FABP is shown in Fig. 1. Levels of hsCRP were higher in the ME/CFS population in comparison to healthy controls (1.38 and 1.21 mg/L, respectively), but the difference was not statistically significant (P = 0.15, Fig. 1a, Table 2).

Fig. 1 Microbial translocation, gastrointestinal tract damage, and evidence for direct LPS stimulation in vivo in ME/CFS: plasma levels of hsCRP (a), LPS (b), I-FABP (c), sCD14 (d), and LBP (e) determined in our cohorts of controls and ME/CFS diagnosed individuals. p values were calculated by the Wilcoxon-Mann-Whitney U test Full size image

Table 2 Plasma levels of markers of inflammation (hsCRP), microbial translocation (LPS, sCD14, and LBP) and gastrointestinal damage (I-FABP) in ME/CFS and healthy individuals Full size table

ME/CFS patients had significantly higher plasma LPS levels than healthy individuals (median ME/CFS—119.43 pg/mL vs. controls—74.74 pg/mL, P < 0.0005, Fig. 1b and Table 2). The median plasma I-FABP level was 341.9 pg/mL in the ME/CFS group and 301 pg/mL in the healthy group. Though the median I-FABP levels in the ME/CFS group was higher than that of the healthy group, the difference was not statistically significant (P = 0.27, Fig. 1c, Table 2).

To obtain further information concerning chronic LPS stimulation in vivo, we also measured plasma sCD14 levels and plasma LBP, which is produced by gastrointestinal and hepatic epithelial cells. Thus, increased LPS in the circulation promote hepatic synthesis of LBP, a plasma protein that increases the binding of LPS to CD14. sCD14 and LBP concentrations in both groups are shown in Fig. 1. For the ME/CFS cohort the median plasma sCD14 concentration was 1.97 ug/mL, and the median LBP plasma concentration was 17.68 ug/mL. These values were significantly different from the plasma sCD14 and LBP concentrations of the healthy volunteers (1.36 ug/mL; P < 0.0005 and 12.32 ug/mL; P < 0.0005, respectively) (Fig. 1d, e, Table 2).

Next, we analyzed the associations among biomarker measurements in the ME/CFS population. As can be seen in Fig. 2a, b plasma LPS levels correlated positively with levels of sCD14 and LBP (r = 0.347, P < 0.01 and r = 0.487, P < 0.01, respectively), consistent with stimulation of sCD14 production by LPS in vivo. In addition, we found a strong significant correlation between plasma sCD14 and hsCRP and sCD14 and LBP; high levels of sCD14 were associated with high levels of hsCRP (r = 0.507, P < 0.01) and LBP (r = 0.578, P < 0.01) (Fig. 2c, d). We also analyzed whether enterocyte damage (i.e., I-FABP levels) was associated with the proposed microbial translocation markers LPS, sCD14, and LBP. We found no relationship between I-FABP and LPS levels (r = −0.125; P = 0.278), I-FABP and sCD14 levels (r = −0.117; P = 0.310), or I-FABP and LBP levels (r = −0.08; P = 0.488).

Fig. 2 Correlation between plasma levels of LPS and sCD14 (a), plasma levels of LPS and LBP (b), plasma levels of sCD14 and LBP (c), and plasma levels of hsCRP and sCD14 (d) in the ME/CFS population. Spearman’s rank test was used to determine correlations Full size image

Stool microbiota of ME/CFS patients exhibit reduced diversity and different composition than healthy controls

The hypervariable V4 region of 16S rRNA genes was sequenced from fecal samples of individuals with ME/CFS (n = 48) and healthy individuals (n = 39). A total of 8,534,117 high-quality and classifiable reads were generated from all samples, with an average of 98,093 ± 29,231 reads per sample. Binning sequences using a pairwise identity threshold of 97 %, we obtained an average of 1330 ± 423 operational taxonomic units (OTUs) per sample. The sequence-based rarefaction curves based on the Phylogenetic Diversity (PD) metric were nearly asymptotic and a Wilcoxon rank-sum test demonstrated a statistical difference in the diversity of ME/CFS and healthy individuals (P = 0.004, W = 1268) (Fig. 3a).

Fig. 3 Rarefaction curves and confusion matrix. a Rarefaction curves for the microbiota of healthy individuals and ME/CFS patients (each group was rarefied to the number of sequences of the less-sequenced sample, i.e., 32223 sequences). The p value was calculated by the Wilcoxon rank-sum test and b comparison of alpha diversity indexes in ME/CFS and healthy individuals Full size image

We examined the number of “observed species,” i.e., the number of 97 % ID OTUs observed in 32,223 sequences, the estimators of community evenness (Shannon H), and richness (Chao1 and PD) in the two group of samples. ME/CFS samples had a significant overall lower microbial diversity, with lower evenness (H = 5.33 ± 0.93 vs. 5.92 ± 0.93, P = 0.004), and lower richness (observed species, 1204 ± 351 vs. 1486 ± 456; Chao1, 2363 ± 704 vs. 2918 ± 885, P = 0.002; PD, 61.6 ± 16.7 vs. 73.4 ± 19.04, P = 0.004) (Fig. 3b).

To evaluate overall differences in beta-diversity between the microbiomes, we applied Principal Component Analysis (PCoA) to weighted and unweighted UniFrac distance metric matrices generated for the sample set. Within the microbial community cluster, there appears to be no clear difference in beta-diversity between the ME/CFS group and healthy group using both weighted (Additional file 2: Figure S2a) and unweighted (Additional file 2: Figure S2b) UniFrac distance matrices. None of the other parameters tested, i.e., sex, BMI, or clinical data revealed clustering (data not shown). Because beta-diversity clustering as measured by UniFrac shows how dissimilar overall community structure is between samples, the samples may not cluster in a manner that reflects differences detected at the OTU level, or the overall alpha diversity within groups.

The overall microbial composition for ME/CFS and controls differed at the phylum and family levels (Fig. 4a, b), although none of these differences were statistically significant after multiple test correction. The two largest phyla represented in each dataset of healthy and ME/CFS-afflicted individuals were Firmicutes and Bacteroidetes. In healthy individuals, this corresponded to 46 and 45 % respectively of the rarified 16S rRNA sequences. Also, Proteobacteria made up the next largest represented phylum (3.6 %), with Verrucomicrobia and Actinobacteria in relatively low relative abundance (2.1 and 1.6 %, respectively). At the phylum level, the abundance of the Bacteroidetes was comparable (52 %) in both datasets (Fig. 4a). ME/CFS samples showed lower relative abundance of Firmicutes (35 %) (Fig. 4a) and higher relative abundance of Proteobacteria (8 %), due almost entirely to a twofold increase in the Proteobacteria family Enterobacteriaceae (6 vs. 3 % for ME/CFS and healthy individuals, respectively) (Fig. 4b). Within the Firmicutes, at the family level, Ruminococcaceae were lower in the ME/CFS samples (16 vs. 11 % in ME/CFS and healthy individuals respectively) (Fig. 4b), whereas Lachnospiraceae were similar among both datasets (16 % for both healthy and ME/CFS samples). Some differences were noted between cases and controls in family members of the Bacteroidetes, i.e., Bacteroidaceae (35 vs. 43 %), Rickenellaceae (3 vs. 4 %), and Prevotellaceae (3.2 vs. 0.7 %). Finally, within the Actinobacteria, Bifidobacteriaceae were lower in the ME/CFS samples (1 vs. 0.5 %).

Fig. 4 Composition of the gut microbiome of healthy individuals and ME/CFS patients. Relative abundance of phylum-level (a) and family-level (b) gut microbial taxa Full size image

At the OTU level, 40 OTUs were found to be significantly different between groups after multiple testing correction. The majority of them belonged to the Firmicutes phylum, including members of the Ruminococcaceae family such as Oscillospira spp. (q = 0.016), Faecalibacterium prausnitzii (q = 0.014), and Ruminococcus spp. (q = 0.014) and members of the Lachnospiraceae, i.e., Coprococcus spp. (q = 0.014). Other OTUs included members of the Actinobacteria such as Eggerthella lenta (q = 0.014) and Collinsella aerofaciens (q = 0.014).

These significant differences were further confirmed by LEfSe analysis, which uses linear discriminant analysis (LDA) coupled with effect size measurements to identify bacterial taxa whose sequences are differentially abundant between ME/CFS and healthy individuals. In addition to detecting significant features, LEfSe also ranks features by effect size, which put features explaining most of the biological difference at top (Segata et al. 2011). LEfSe identified 24 discriminative features (genus level, LDA score >2) whose relative abundance varied significantly among fecal samples taken from the ME/CFS and healthy groups (Fig. 5). ME/CFS microbiota were enriched with an unclassified member of the Desulfohalobacteriaceae and genera from the Firmicutes phylum, i.e., Oscillospira, Lactococcus, Anaerotruncus and Coprobacillus and Eggerthella, a member of the Actinobacteria phylum (P < 0.05, Fig. 5). Eighteen genera were enriched in the control group compared to the ME/CFS group (Fig. 5) with members mainly belonging to the Firmicutes phylum. We observed that members of the Ruminococcaeae and Bifidobacteriaceae, i.e., Faecalibacterium and Bifidobacterium, respectively, were significantly increased in healthy individuals (P = 0.03 and 0.04, respectively).

Fig. 5 Histogram of the LDA scores computed for genera differentially abundant between ME/CFS and healthy individuals. ME/CFS-enriched genera are indicated with a positive LDA score, and genera enriched in healthy individuals have a negative score. The LDA score indicates the effect size and ranking of each differentially abundant taxon Full size image

Classifying subjects into patients vs. controls from inflammatory markers and microbiome data

Using a machine learning approach, samples were mostly successfully classified into healthy and ME/CFS groups, with the highest proportion of samples correctly classified when genus-level taxa along with data from the inflammatory markers were used in the analysis. With 97 % ID OTUs used in the analysis, 82 % of the samples could be correctly classified (standard deviation of 0.14). With OTUs collapsed at the species level, the average accuracy was 0.80 with a standard deviation of 0.11. Collapsing taxonomy to the genus level, individuals with ME/CFS were classified correctly and separately from the healthy group with an average success rate of 0.82 ± 0.12. The receiver operating curves, the AUC ROC value for the ME/CFS samples (0.89), and the confusion matrix are presented in Fig. 6. The feature importance scores for the genus-level analysis, which shows the relative importance of clinical values and microbial abundances, are available in Additional file 3: Table S1. Additionally, processing microbial sequencing data without including BMI and blood inflammatory marker levels results in 70, 75, and 72 % classification accuracy for genus, species, and OTU-level data respectively (confusion matrices available in Additional file 4: Figure S3).