Additional file 1: Table S1. of Clinically proven mtDNA mutations are not common in those with chronic fatigue syndrome

Contains the mtDNA variants from participants. (XLSX 24 kb

Interferon type I signature and fatigue.

<p>(A) The IFN score ranges for the 133 patients. (B) The Fatigue VAS scores for the IFN-active and IFN-inactive groups. (C) The ESSDAI scores for the IFN-active and IFN-inactive groups.</p

The characteristics of the patients.

<p>A heatmap of the clinical scores for the 133 patients included in this study. The values have been scaled between zero (absent) and one (worst). ESSDAI = EULAR Sjögren’s Syndrome Disease Activity Index, SSDDI = Sjögren’s Syndrome Disease Damage Index, ESSPRI = EULAR Sjögren’s Syndrome Patient Reported Index, HAD = Hospital Anxiety and Depression, PROFAD = Profile of Fatigue and Discomfort, VAS = Visual Analogue Scale.</p

Support vector machine (SVM) classification of fatigue groups.

<p>The receiver operator characteristic curves for the SVM output. Ten curves are shown on each plot. The area under the curve (AUC) is calculated as the mean over the ten curves. (A) All 181 enriched pathway genes as input. (B) The 55 leading edge genes as input.</p

Correction for other clinical factors.

<p>Volcano plots for the Fatigue VAS fatigue groups corrected for clinical factors: (A) Age at UKPSSR cohort recruitment. (B) Disease activity measured using the EULAR Sjögren’s Syndrome Disease Activity Index. (C) Disease damage measured using the Sjögren’s Syndrome Disease Damage Index. (D) The EULAR Sjögren’s Syndrome Patient Reported Index dryness sub-domain. (E) The EULAR Sjögren’s Syndrome Patient Reported Index pain sub-domain. (F) Anxiety measured using the Hospital Anxiety and Depression scale. (G) Depression measured using the Hospital Anxiety and Depression scale. (H) Pain and depression (E & G). (I) Pain, depression, dryness and anxiety (D-G). (J) All seven factors (A-G). No significantly differentially expressed genes were identified following any correction.</p

A workflow of the gene expression analysis.

<p>The gene expression data were analysed to produce a list of fatigue-related features which were used as inputs for a support vector machine classifier of fatigue. 1. Differentially expressed genes were identified between fatigue groups. 2. Linear regression was used to analyse fatigue as a continuous variable. 3. The interferon type I signature was calculated for all the patients and compared to fatigue levels. 4. Gene set enrichment analysis was carried out using the high and low fatigue groups. 5. A support vector machine classifier was created using fatigue-related features as inputs and its performance assessed using receiver-operator characteristic (ROC) curves.</p

Composition of independent cohorts used in the genetic association analyses.

<p>Composition of independent cohorts used in the genetic association analyses.</p

Differentially expressed transcripts between 115 anti-Ro/SSA positive SS cases and 56 controls identified through transcriptome profiling.

<p>(A) We identified 73 genes (represented by 83 probes on the heatmap) differentially expressed between anti-Ro/SSA positive SS cases and healthy controls (absolute FC >2 and <i>q</i><0.05). Among the differentially expressed genes, 57 were type I IFN-regulated genes (black bar on right) and formed an IFN signature where most genes were overexpressed in SS patients (yellow indicates overexpressed genes compared to controls). (B) The 57 differentially expressed type I IFN-regulated genes were re-clustered in anti-Ro/SSA positive SS cases using <i>k</i>-means (<i>k</i> = 3) algorithm and heterogeneity of the IFN signature levels in anti-Ro/SSA positive SS cases was observed.</p

Functional characterizations of <i>OAS1</i> isoforms.

<p>(A) Protein expression of OAS1 isoforms was evaluated in EBV-transformed B cells from SS patients (four independent samples from each genotype group) using anti-OAS1 antibody targeting the shared epitope of all the isoforms. The stimulated cells were treated with universal type I IFN (1500U/ml) for 24hrs. The p44 isoform was not detectable using western-blot due to its low expression. The right panel shows quantified band intensity normalized to the GAPDH in each sample. (B) The transcript levels of each <i>OAS1</i> isoform from the same sets of cells described above were determined using real-time PCR. Consistent with the RNA-seq results, the SS-associated risk allele A of rs10774671 was correlated with decreased levels of p46 and increased expression of the p42, p48, and p44 isoforms (significance levels are shown at the bottom). The transcript levels of all the isoforms significantly increased after IFN stimulation (two-tailed <i>t</i> test); however, only p46 had increased protein production after IFN stimulation. (Significance level: ** <i>P</i><0.01; *** <i>P</i><0.001) (C) Individual isoforms of <i>OAS1</i> tagged with Xpress epitope were cloned and transfected into HEK 293T cells for 48hrs. The p48 and p44 isoforms had impaired protein expression compared to p46 and p42, although their transcript levels were equivalent as determined by real-time PCR (n = 4; normalized to <i>HMBS</i>). (D) The full-length and truncated <i>OAS1</i> p48 and p44 isoforms were cloned into HEK 293T cells. Western-blot indicated the lack of expression of the full-length p48 and p44 isoforms, whereas the truncation of both isoform transcripts (T2 and T4) was able to restore protein expression. (E) The 3' alternatively spliced terminus of different <i>OAS1</i> isoforms were linked to the 3'-end of GFP to observe their influence on GFP protein expression in HEK 293T cells. The 3'-terminus from the p48 and p44 isoforms resulted in decreased expression of GFP.</p

Study design.

<p>To evaluate genetic factors involved in the dysregulation of type I IFN signaling in SS, we first compared transcriptional profiles between anti-Ro/SSA positive SS cases and controls to identify genes that make up the IFN signature in SS. We then performed genetic association analysis for variants in the regions of the differentially expressed genes. By integrating transcriptome data with genotype data, <i>cis</i>-eQTL analysis was performed for SS-associated SNPs to evaluate their role in gene dysregulation. This genomic convergence approach resulted in increased power to identify and prioritize disease susceptibility genes for further genetic replication and functional studies.</p