Thiol metabolite levels in plasma.

<p>Thiol / Thioether metabolite levels were analyzed via HPLC with electrochemical detection before (BL) and after sleep deprivation (SD) in participants (Mean +/- SEM). GSH / GSSG ratio indicates oxidative stress. HCY—homocysteine, Cys- cysteine were measured in uM. ATP measurement. ATP was measured using commercially available kit and expressed in umol/ L of plasma. Asterisk indicates a significant difference from baseline * = p < 0.05, ** = p < 0.01.</p

Cortisol.

<p>Moring cortisol levels were significantly lower after sleep deprivation (mean = 0.35, SD = 0.22) relative to the morning of baseline testing (mean = 0.63, SD = 0.24), t(18) = 6.61, p < 0.01.</p

Redox-methylation pathway.

<p>Methionine synthase contains a redox-active methylcobalamin cofactor. Under oxidative stress, this cofactor becomes oxidized, limiting methionine synthase activity and can affect the levels of global DNA Methylation Under these conditions, homocysteine can be condensed with serine to form cystathionine and then with cysteine to support GSH synthesis. Another source of the cysteine is through the Excitatory Amino Acid Transport (EAAT3), which is the major source of cysteine especially in the neuronal cells. Cellular redox state is indicated by the GSH/GSSG ratio.</p

Actigraphy measures.

<p>Sleep behavior was objectively verified the week prior to sleep deprivation through actigraph monitoring. Top left: actigraphy-recorded time-in-bed confirmed that the participants generally adhered to the instruction to sleep 8 hours the week prior to sleep deprivation (mean = 8.03, SD = 1.12). Top Right: Total sleep actigraphy-recorded sleep time was 6 hours and 10 min (SD = 1.04). Bottom Left: The average sleep latency was 16 minutes (SD = 10.29). Bottom Right: the average sleep efficiency was 82.64% (SD = 4.71).</p

Identification of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome-associated DNA methylation patterns

<div><p>Background</p><p>Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex condition involving multiple organ systems and characterized by persistent/relapsing debilitating fatigue, immune dysfunction, neurological problems, and other symptoms not curable for at least 6 months. Disruption of DNA methylation patterns has been tied to various immune and neurological diseases; however, its status in ME/CFS remains uncertain. Our study aimed at identifying changes in the DNA methylation patterns that associate with ME/CFS.</p><p>Methods</p><p>We extracted genomic DNA from peripheral blood mononuclear cells from 13 ME/CFS study subjects and 12 healthy controls and measured global DNA methylation by ELISA-like method and site-specific methylation status using Illumina MethylationEPIC microarrays. Pyrosequencing validation included 33 ME/CFS cases and 31 controls from two geographically distant cohorts.</p><p>Results</p><p>Global DNA methylation levels of ME/CFS cases were similar to those of controls. However, microarray-based approach allowed detection of 17,296 differentially methylated CpG sites in 6,368 genes across regulatory elements and within coding regions of genes. Analysis of DNA methylation in promoter regions revealed 307 differentially methylated promoters. Ingenuity pathway analysis indicated that genes associated with differentially methylated promoters participated in at least 15 different pathways mostly related to cell signaling with a strong immune component.</p><p>Conclusions</p><p>This is the first study that has explored genome-wide epigenetic changes associated with ME/CFS using the advanced Illumina MethylationEPIC microarrays covering about 850,000 CpG sites in two geographically distant cohorts of ME/CFS cases and matched controls. Our results are aligned with previous studies that indicate a dysregulation of the immune system in ME/CFS. They also suggest a potential role of epigenetic de-regulation in the pathobiology of ME/CFS. We propose screening of larger cohorts of ME/CFS cases to determine the external validity of these epigenetic changes in order to implement them as possible diagnostic markers in clinical setting.</p></div

Unsupervised hierarchical clustering of 500 most variable CpG sites derived from samples distinguishes ME/CFS (N = 13) cases and controls (N = 12).

<p>500 DMS above and below mean level are represented by blue and yellow across all cases and were used to cluster cases. As shown above, 12 of 13 ME/CFS cases (blue on color-coded bar above the dendrogram) cluster together (left dendrogram branch) and 12 of the 12 controls (blue color-coded bar) cluster together (right dendrogram branch), resulting in a divergence of these sub-phenotypes.</p

Demographic information and SF-36 results for all ME/CFS cases (n = 33) and controls (n = 31) that participated in DNA methylation analysis and validation.

<p>*—p<0.05, Student’s t-test, ME/CFS cases versus controls subjects. Data are shown as mean ± standard error of mean.</p

Distribution of differentially methylated sites according to genic location and to CpG island relation.

<p>(A) Distribution of DMS according to genic location. Relative percentages of DMS (both hypo-methylated and hyper-methylated CpG sites between ME/CFS cases and controls) are reported for each genic body location: gene regulatory elements (regulatory: TSS1500, TSS200, 5’ UTR, 3’ UTR), the coding regions of genes (gene body), as well as all Intergenic and 1st Exon gene body regions. (B) Distribution of DMS according to their location in relation to CpG islands. Relative percentages of DMS (both hypo-methylated and hyper-methylated) are reported. CpG islands with clustered CpG sites; 2 kb upstream and downstream of CpG islands (N, S Shores); 2 kb upstream and downstream of CpG shores (N, S Shelves).</p

Distribution of differentially methylated promoter regions in ME/CFS according to gene biotype.

<p>In RnBeads package, promoters are defined as regions 1500 bp upstream of TSS and 500 bp downstream of TSS. Criteria for DMPs were: FDR ≤ 0.1 and mean difference > 0.05. The distribution of these DMPs according to the gene biotype: protein-coding genes and regulatory RNA genes including short non-coding RNAs (miRNAs, small nuclear RNAs, small nucleolar RNAs), long non-coding RNAs (including antisense RNAs and intergenic RNAs) and pseudogenes.</p

Bisulfite-conversion followed by pyrosequencing (PS) validation of DMPs identified by Illumina MethylationEPIC microarrays (EPIC).

<p>Average methylation level of CpG sites within promoters of TABPB and ZBTB18 were assessed using pyrosequencing. Corresponding probes for TABPB: CpG1—cg04415168 (TSS1500), CpG2—cg10376053 (TSS1500), CpG3—cg14288848 (TSS1500), CpG4—cg17055704 (TSS1500), CpG5—cg14473643 (TSS1500), CpG6—cg14309283 (TSS1500). Corresponding probes for ZBTB18: CpG1—cg16399365 (TSS1500), CpG2—cg15896892 (TSS1500), CpG6—cg19698993 (TSS1500). CpG3, CpG4 and CpG5 are not printed on the Illumina MethylationEPIC microarrays and are located in ZBTB18 promoter between CpG2 and CpG6. * = FDR ≤ 0.05 for EPIC; * = p ≤ 0.05 for PS. Error bars represent the standard error of the mean.</p