Gene Expression in CFS Table 1. Descriptive characteristics for 44 pairs of monozygotic twins discordant for chronic fatiguing illness. Variable Met criteria for CFS Met criteria for ICF Female sex Median age at evaluation, IQR Median body mass index, IQR Median SF-36 physical function, IQR Median SF-36 mental function, IQR Median current fatigue by VAS, IQR Median duration of fatigue, IQR Sudden onset of fatigue Affected twins 32/44, 73% 12/44, 27% 39/44, 89% 51, 39–59 years 24.6, 22–30 kg/m2 41.2, 28–48 38.9, 29–47 69, 49–77 7.6, 4–16 years 2/44, 4.5% Unaffected co-twins Statistical comparison 0/44, 0% 0/44, 0% 39/44, 89% 51, 39–59 years 24.4, 22–31 kg/m2 48.2, 40–52 51.6, 40–56 19, 10–51 By design By design Identical by design Identical by design Paired t43 = 0.2, p = 0.87 Paired t43 = 3.0, p = 0.004 Paired t43 = 5.0, p = 161025 Paired t43 = 27.2, p = 661029 – – Abbreviations. CFS = chronic fatigue syndrome, ICF = idiopathic chronic fatigue, IQR = inter-quartile range, VAS = visual analog scale (0–100, higher means greater fatigue at clinical examination). doi:10.1371/journal.pone.0005805.t001 fatiguing illness. For verification, we used qRT-PCR to assess the expression of seven genes selected from the CFS gene expression literature and our empirical findings (ANKLE2, BLKE, BRD1, CPA3, DCTN1, ICAM, and ORC). All p-values from paired t-tests contrasting affected and unaffected monozygotic twins were $0.26. It is possible that functionally-related genes might have important set-wise gene expression changes with no individual transcript meeting criteria for significance. As a hypothesis- generating analysis, we used SAFE [31] to conduct analyses of gene groupings defined by KEGG pathways [28], GO keywords [29] (biological process, cellular component, and molecular function), and PFAM protein family groupings [30]. Broadly, these analyses revealed significant differences in cell replication processes and amino acid and lipid metabolic pathways (Table S1). These results do not map directly onto current major theories of CFS pathogenesis and should be regarded as hypothesis- generating. Discussion Main Finding The overarching goal in this study was to attempt to identify one or more biomarkers for chronic fatiguing illness via a compre- hensive search of the ‘‘transcriptome’’ in an accessible tissue (peripheral blood leukocytes, PBLs) plausibly involved in the pathophysiology of this idiopathic syndrome. We attempted to correct methodological issues in prior reports by careful control of sources of bias (e.g., by studying discordant monozygotic twin pairs, use of RNA stabilizing media, and standardized sampling conditions). We found no evidence of differential PBL gene expression that characterized the presence or absence of CFS or ICF. Therefore, unlike most prior published studies, we did not find evidence of a gene expression biomarker for chronic fatiguing illness. Methodological Issue: genetic matching in gene expression studies These results may hold a lesson for case-control gene expression studies in humans. There are certainly examples where transcrip- tomic studies have yielded results that have the potential to improve disease prognosis and management (e.g., breast cancer) [34]; however, gene expression studies have the potential to yield false positive findings if the ancestry of cases and controls are not appropriately matched. Genetic background is usually not taken into consideration although gene expression is can be both heritable and under strong genetic control [35]. Relatively low- resolution studies in immortalized PBLs suggest that hundreds of human genes are under relatively strong genetic control by common genetic variants (e.g., the single nucleotide polymor- phism ‘‘rs407257’’ is strongly associated (p,10266) with the expression level of glutathione S-transferase theta 1, GSTT1) [36]. The genetic variant rs407257 is variable in human populations (allele frequencies of 0.72, 0.64, and 0.39 in African, East Asian, and European samples) [37]. If case and control subjects are not extremely well-matched for genetic background (including for location within Europe) highly significant differ- ences could occur because of bias from inappropriate case- control matching. This concern is particularly important for studies of PBLs as genes whose expression is under strong genetic control [36] are highly enriched for genes expressed in lymphoid tissue and lymphocyte cell populations (analyses using DAVID [38], data not shown). Use of discordant monozygotic twins represents the best control for genetic background currently possible in humans. Assuming identify at the DNA level and control for experimental bias, gene expression differences in discordant monozygotic twins can be cleanly attributed to disease state. It is reasonable to consider if use of discordant monozygotic twins represents ‘‘over-matching’’. In comparisons of unrelated cases and controls, gene expression differences are an amalgam of disease state, the RNA-level impact of genetic loci causal to the trait, and the effects of case-control genetic mismatching (i.e., non-causal loci that differ in frequency between cases and controls and which have strong control on gene expression). Use of discordant monozygotic twins yields more interpretable results particularly as there are large numbers of non- causal loci under genetic control. We would also argue that a gene expression study is a poor way to identify genetic loci causal to a disease when an alternative study design (the genome-wide association study) has been so successful [39]. Conclusions We were unable to identify a biomarker for chronic fatiguing illness in the transcriptome of peripheral blood leukocytes suggesting that positive findings in prior studies may have resulted from experimental bias. PLoS ONE | www.plosone.org 3 June 2009 | Volume 4 | Issue 6 | e5805