Kerr et al: Seven genomic subtypes of CFS/ME
Geplaatst: 13 dec 2007, 14:16
Seven genomic subtypes of Chronic Fatigue Syndrome / Myalgic Encephalomyelitis (CFS/ME): a detailed analysis of gene networks and clinical phenotypes
Jonathan R Kerr,1,2 Beverley Burke,1* Robert Petty,1* John Gough,1,2 David Fear,3 Derek L
Mattey,4 John S Axford,1,2 Angus G Dalgleish,1 David J Nutt.5
1Department of Cellular & Molecular Medicine, St George’s University of London, London,UK; Sir Joseph Hotung Centre for Musculoskeletal Disorders; 3Dept of Asthma, Allergy and
Respiratory Sciences, King’s College London, London, UK; 4Staffordshire Rheumatology Centre, Stoke on Trent, UK; 5Psychopharmacology Unit, Dept of Community Based Medicine,
University of Bristol, Bristol, UK.
*Robert Petty and Beverley Burke made equal contributions to this paper.
JCP Online First, published on December 5, 2007 as 10.1136/jcp.2007.053553
Abstract
Chronic Fatigue Syndrome / myalgic encephalomyelitis (CFS/ME) is a multisystem disease,the pathogenesis of which remains undetermined. We have recently reported a study of gene expression which identified differential expression of 88 human genes in patients with CFS/ME. Clustering of QPCR data from CFS/ME patients revealed 7 distinct subtypes with distinct differences in SF-36 scores, clinical phenotypes and severity. In this study, for each CFS/ME subtype, we determined those genes whose expression differed significantly from that of normal blood donors, and then determined gene interactions, disease associations and molecular and cellular functions of those gene sets. Genomic analysis was then related
to clinical data for each CFS/ME subtype. Genomic analysis revealed some common (neurological, haematological, cancer) and some distinct (metabolic, endocrine,
cardiovascular, immunological, inflammatory) disease associations among the subtypes. Subtypes 1, 2 and 7 were the most severe, and subtype 3 was the mildest. Clinical features of each subtype were as follows: subtype 1 (cognitive, musculoskeletal, sleep, anxiety / depression); subtype 2 (musculoskeletal, pain, anxiety / depression); subtype 3 (mild); subtype 4 (cognitive); subtype 5 (musculoskeletal, gastrointestinal); subtype 6 (postexertional); subtype 7 (pain, infectious, musculoskeletal, sleep, neurological, gastrointestinal, neurocognitive, anxiety / depression). It is particularly interesting that in these genomically derived subtypes, there were distinct clinical syndromes and that those which
were most severe were also those with anxiety / depression, as would be expected in a disease with a biological basis.
Introduction
Chronic Fatigue Syndrome / Myalgic Encephalomyelitis (CFS/ME) is a disease characterised by severe and debilitating fatigue, sleep abnormalities, impaired memory and concentration,and musculoskeletal pain.1 In the Western world, the population prevalence is estimated to be
of the order of 0.5%.2,3 Research studies have identified various features relevant to the pathogenesis of CFS/ME such as viral infection, immune abnormalities and immune
activation, exposure to toxins, chemicals and pesticides, stress, hypotension, lymphocyte abnormalities and neuroendocrine dysfunction. However, the precise underlying disease mechanisms and means by which these abnormalities inter-relate in CFS/ME patients, remain to be clarified.4,5
We have previously described a study of gene expression in peripheral blood from 25 CFS/ME patients diagnosed according to the Centers for Disease Control (CDC) diagnostic criteria and 50 normal blood donors using the Affymetrix U133+2 microarray. Genes showing differential expression were further analysed using quantitative PCR (QPCR) in 55 CFS/ME patients and 75 normal blood donors. Differential expression was confirmed for 88 genes, 85
of which were upregulated and 3 downregulated. Highly represented functions were haematological disease and function, immunological disease and function, cancer, cell death,immune response and infection. Clustering of QPCR data from CFS/ME patients revealed 7 distinct subtypes with distinct differences in SF-36 scores, clinical phenotypes and severity.6
In this study, we have determined for each CFS subtype, the fold-difference of each of the 88 CFS-associated genes compared with normal persons. Using a fold-difference cut-off of =1.5, we have then determined those genes which are differentially expressed in each CFS subtype. For each subtype, we report respective gene functions / pathways gene interactions, and disease associations, and relate these to the clinical phenotype details of each.4
Methods
Subjects and clinical characterisation Analyses in this paper are based upon clinical and genomic data from CFS/ME patients whose blood was used for QPCR confirmation of microarray data, as previously reported.6 In total, 55 such patients were enrolled from clinics in Dorset, UK; Bristol, UK; London, UK; and New York City, USA (one patient from Leicester, UK, was managed by a clinic in London). These cases were diagnosed according to the CDC diagnostic criteria for CFS/ME.1 Patients with psychiatric disease were excluded using the Minnesota International Neuropsychiatric Interview (MINI), thus ensuring that none of our patients was suffering from major psychiatric
disease or abuse of alcohol or other drugs. In addition, patients who smoked in the previous year, or were currently taking (or were within 3 months of taking) antibiotics, steroids or antidepressants were excluded from the study.
For all enrolled subjects, according to the recommendations of the International CFS Study
Group,7 severity of physical and mental fatigue was assessed using the Chalder Fatigue Scale;8 level of disability was assessed using the Medical Outcomes Survey Short Form-36 (SF-36); accompanying symptoms were characterised using the Somatic and Psychological
Health Report (SPHERE); sleep abnormalities were assessed using the Pittsburgh Sleep Questionnaire; and assessment of type and severity of pain was performed using the McGill
Pain Questionnaire. For the CFS/ME patients, neurocognitive testing was performed using the Spatial Span (SSP) and Verbal Recognition Memory (VRM) modules of the Cantab software (Cambridge Cognition, UK), which showed abnormal results in CFS/ME.6,9 For each CFS patient, the severity of particular symptoms and level of function was taken from the above questionnaires.
Then for each CFS subtype which was derived by clustering of QPCR data as previously described,6 mean values for each symptom and score were calculated and compared between the subtypes. Analysis of variance (ANOVA) was used to determine the significance of differences in individual SF-36 domain scores between CFS subtypes.
Patients and controls gave written consent according to guidance of the Wandsworth Research Ethics Committee (approval number 05/Q0803/137). For the New York patients,
approval of the local Institutional Review Board was obtained. The human experimentation guidelines of the US Department of Health and Human Services were followed in this study.
Determination of differential expression of human genes in each CFS/ME subtype The threshold cycle (Ct) for each test gene in each sample was compared to a calibrator sample to calculate a ?Ct value. ?Ct values were then normalised to the Ct value for an endogenous control gene, lyceraldehyde-3-phosphate dehydrogenase (GAPDH) in respective samples to give the ??Ct values. Relative quantities (RQ) (2-??Ct) of each mRNA of interest were then calculated. Samples showing a difference between minimum and maximum RQ values of =100 (indicating poor replicate concordance) were excluded. The ttest was used to compare RQ values for the CFS/ME patients with those of the controls. Genes whose mean RQ values differed between the groups (at P=0.05) were included in our CFS/ME-associated gene signature.6 RQ values for all 88 CFS/ME-associated genes were
were normalised and clustered using Genesis software.10
For each CFS subtype, mean relative quantity (RQ) values were calculated. Then, for each gene, the mean RQ value for each CFS subtype was divided by the mean RQ value of the normal blood donors, to provide fold-difference values for each CFS subtype. For each subtype, genes were included for analysis assuming they showed fold-difference values (mean RQ in CFS subtype / mean RQ in Normal) in QPCR experiments of =1.5. Thus an 5 individual gene list was generated for each CFS subtype within the 88 gene signature for CFS. Mean fold-difference values were clustered using Cluster version 2.11 software (without
normalization) and visualised using Treeview version 1.60 software.11
Analysis of gene function and interaction in each CFS/ME subtype
Each of these subtype-specific gene lists was analysed for gene function and interaction using Ingenuity Pathways Analysis (IPA) software (Ingenuity, Redwood City, CA, USA) in order to link CFS/ME-subtype-associated genes into networks based on recognised interactions, and to discern the top associated diseases and disorders, molecular and cellular functions, associated physiological system development and function and canonical pathways. 6
Results
Subjects and clinical characterisation Clinical and genomic data from a total of 55 CFS/ME patients fulfilling CDC diagnostic criteria were used for this study. Of these, 19 were male, and 36 were female, with an overall mean age of 41.6 years and a mean duration of disease of 3.2 years. Additional clinical details are provided elsewhere.6 This study included several CFS/ME patients whose disease was severe and necessitated bed rest for much of the day, and patients who were able to attend
an out-patient clinic. Normal blood donors were used as a comparison group and clinical data for these is available elsewhere.6
Genomic CFS/ME subtypes.
AS previously reported, clustering of QPCR data revealed the presence of 7 genomic CFS subtypes with distinct profiles of gene expression within the 88-gene CFS gene signature.6 Fold-difference values (mean RQ in CFS / mean RQ in normal) for all CFS patients and for each CFS subtype are shown in Table 1. For each subtype, genes with fold-difference values of =1.5 were noted and used in further analysis. This resulted in the following numbers of
differentially-expressed genes in each subtype; 58 (CFS subtype 1), 70 (CFS subtype 2), 48 (CFS subtype 3), 27 (CFS subtype 4), 66 (CFS subtype 5), 69 (CFS subtype 6), 71 (CFS subtype 7), respectively. In table 2, genes without values are those for which there was missing data for particular subtypes.
Analysis of gene function in each CFS/ME subtype
Using Ingenuity Pathways Analysis (IPA) software (Ingenuity, Redwood City, CA, USA), the gene list for each CFS subtype was analysed to determine the most important associated diseases and disorders, molecular and cellular functions, associated physiological system development and function and canonical pathways. The results of this analysis are shown in Table 2. As regards disease associations, analysis revealed some common (neurological,
haematological, cancer) and some distinct (metabolic, endocrine, cardiovascular, immunological, inflammatory) disease associations among the subtypes.
This was also true for cellular and molecular functions, and physiological system development and function analyses (Table 2). As regards the canonical pathways implicated in each subtype, there was more variation between subtypes than for the previous analyses, probably because these assignments are based on fewer genes per pathway. IL-6 signalling was implicated in subtypes 1, 2, 5, 6; B cell receptor signaling was implicated in subtypes 4, 6; oestrogen receptor signaling was implicated in subtypes 7; ephrin receptor signaling was implicated in subtypes 1, 2 and 7; and insulin receptor signaling was implicated in subtypes 3, 4 and 6 (Table 2).
Analysis of gene interaction in each CFS/ME subtype
Gene interaction was assessed for each subtype using IPA software. For each subtype, this analysis generated between 2 and 5 large networks (arbitrarily defined as containing 8 or more CFS-associated genes) based on published gene interactions (data not shown) and a
variable number of smaller networks and single genes for which interactions were not known. For each subtype, all networks, large and small, were combined into a single network, indicating genes found to be upregulated and downregulated and then stratified to show the
subcellular location of each (Figure 3, panels A-G).
Clinical features of each CFS subtype Numbers of patients, mean age and male:female ratio for each subtype were as follows: subtype 1 (2; 27 years; 1:1), subtype 2 (5; 49 years; 4:1), subtype 3 (2; 32 years; 0:2), subtype 4 (19; 44.3 years; 8:11), subtype 5 (7; 51 years; 0:7), subtype 6 (14; 41.1 years; 6:8), subtype 7 (3; 47 years; 0:3). Subtypes 3, 5 and 7 were made up of females only; subtype 2
was predominantly male and the remainder were mixed; age differences were less clearly demarcated. Mean questionnaire scores for each subtype are shown in Figure 1, panels A and B. Clinical symptom severity for each subtype is shown in Figure 1, panel C. CFS subtypes 1 and 7
were the most severe, followed sequentially by subtypes 2, 4, 5 and 6 / 3.
Analysis of variance testing revealed significant differences between groups for the SF36 total score (p = 0.016), social functioning (p = 0.03), and emotional role (p = 0.003), while the difference between groups approached significance for general health (p = 0.08) and mental health (p = 0.08). After adjusting for multiple comparisons significant associations were found between
specific groups and clinical phenotypes. Subtype 7 had most pain, lowest SF-36 scores (along with subtype 1), most severe individual symptoms including swollen glands, sore throat, headaches, etc; subtype 1 had the worst cognition and mental health score, and poor sleep despite having the least pain; subtype 4 had moderate neurocognitive function and cognitive defects combined with moderate levels of bodily pain and sleep problems; subtype 5 had the best mental health but poor neurocognitive function, gastrointestinal complaints and
the most marked muscle weakness and postexertional malaise; and subtype 2 had marked postexertional malaise, muscle pain and joint pain but poor mental health (Figure 1, panels A, B, C).
Summary clinical features of each subtype were as follows: subtype 1 (cognitive, musculoskeletal, sleep, anxiety / depression); subtype 2 (musculoskeletal, pain, anxiety /
depression); subtype 3 (mild); subtype 4 (cognitive); subtype 5 (musculoskeletal, gastrointestinal); subtype 6 (postexertional); subtype 7 (pain, infectious, musculoskeletal, sleep, neurological, gastrointestinal, neurocognitive, anxiety / depression). It is particularly
interesting that in these genomically derived subtypes, there were distinct clinical syndromes and that those which were most severe were also those with anxiety / depression, as would be expected in a disease with a biological basis.
As regards subtype associations with geographical location, subtypes 4 and 6 were predominant in Dorset; subtype 4 was predominant in London and New York, and subtype 5 was predominant in Bristol (Figure 1, panel D).
CFS/ME associated genes which are specifically targeted by existing drugs Within the CFS gene signature, there were 5 human genes which are known to be targeted by one or more existing drugs which are designed or intended for use in other diseases. Based on the expression levels of these 5 genes, these drugs may be predicted to be beneficial for particular CFS subtypes. These genes, corresponding drugs and CFS subtypes are as follows: APP (AAB-001; subtypes 1, 2, 3, 4, 5, 6, 7); CXCR4 (JM1300; subtypes 5, 6); FNTA (lonafarnib, tipifarnib; subtypes 1, 2, 3, 5, 6); IL6ST (tocilizumab; subtypes 1, 2, 5, 6, 7); TNF (golimumab, adalimumab, etanercept, certolizumab pegol, infliximab; subtype 2).
Discussion
This study follows our paper describing differential expression of 88 human genes in CFS patients6 and its purpose is to expand upon the brief description of the genomic and phenotypic aspects of the CFS subtypes given in this earlier paper. It has long been recognized that subtypes of CFS/ME exist, and it has been believed that
these subtypes may, at least in part, reflect particular aetiological factors.12 A symptom-based approach has had some success in identifying musculoskeletal, inflammatory and neurological subtypes,13 however, these groups had only minor differences in overall functional severity in contrast to those of the present study.
It is intriguing that within our 88 gene signature, there are several genes with links to various aetiological triggering factors. For example, virus infection (EIF4G1, EBI2) and organophosphate exposure (Neuropathy Target Esterase (NTE)). EIF4G1 is an eukaryotic translation initiation factor which is bound and cleaved by a range of viruses, including enteroviruses, which both trigger and persistently infect CFS patients.14,15 Whistler and
colleagues have also reported upregulation of EIF4G1 transcript variant 5 (the same variant as we report) in patients with CFS who have rapid (? triggered by virus infection) as compared with insidious onset.16 EIF4G1 is a component of the protein complex, EIF4F, which is crucial in translation.17 These viruses divert EIF4G1 from its utilisation by the cellular machinery to facilitate production of viral proteins.17 EIF4G1 is upregulated in CFS subtypes 1, 2, 3, 4, 6, 7 (Table 1; Figures 2 & 4, panels A, B, C, D, F, G).
Various CFS-associated genes identified have previously been shown to be upregulated in EBV infection, namely NFKB1, EGR1, ETS1, GABPA, CREBBP, CXCR4, EBI2, HIF1A,
JAK1, IL6R, IL7R, PIK3R1. This is very interesting as EBV is a recognized trigger of CFS and is known to reactivate upon stress.18 However, it is difficult to draw conclusions as to the interrelationship of these genes in the different subtypes (Figure 2). The EBV transcription factor BRLF1 was found to be over-represented in the original CFS gene signature, however, this was not tested by PCR.6 The EBV genes, BRLF1 and BZLF1, mediate the switch from latent to lytic phases of EBV infection and during this process they transactivate many human
genes. It is interesting that the BRLF1 gene was identified as being over-represented in the transcription factor analysis, and that specific IgG to the Zebra protein (BZLF1 gene product) has been reported previously in CFS/ME patients.19 EBI2 is a gene which is upregulated 200-fold in EBV infected cells20 and is upregulated in
subtypes 2, 3, 5, 6 and 7, but in none of the normal controls.6
One subject with EBI2 upregulation was a 26 year old female whose CFS had been triggered by laboratorydocumented
EBV infection and who had a chronic course with detectable EBV replication in blood for several years after the acute phase. This suggests the possibility that EBI2 may be
a surrogate marker for ongoing EBV replication in CFS patients, although this remains to be clarified. If this is true, then this would be very useful to inform the decision as to which CFS patients should be treated with valganciclovir, which has been shown to be beneficial in
CFS.21
Three patients had markedly raised levels of NTE, while all normal controls had uniformly low levels; CFS subtypes with significantly raised NTE levels were 1, 2, 5 and 7, of which subtypes 1, 2 and 7 were the most severely affected subtypes. We have previously documented upregulation of NTE in CFS.22 NTE is the primary site of action of organophosphate (OP) compounds, such as sarin, which cause axonal degeneration and paralysis resulting from inactivation of its serine esterase activity23 and in the adult chicken nervous system, OP-modified NTE initiates neurodegeneration. Exposure to OP compounds
may trigger CFS/ME24 and Gulf War Illness (GWI).25
IL10RA is a gene which is critical for T cell activation and immune system homeostasis as polymorphisms in it have been shown to be associated with development of lymphoma,
COPD, autoimmunity, severity of hepatitis C infection, and multiple sclerosis.26-30 In the present study it was upregulated,6 although we have previously found it to be downregulated in CFS patients.22
There were more subjects in our pilot study19 that were bed-bound than in the present study,6 and IL10RA levels appear to be a marker of severity in CFS (as they are
closely correlated with SF-36 general health score), with lower levels reflecting increasing severity (data not shown). It is interesting that disease associations identified in the various subtypes are mostly those
which are already recognised in CFS. However, for any one disease association, there are important variations between the subtypes. For example, for ‘neurological disease’, which applies to all subtypes, the number of genes in this category varies from subtype to subtype
(Table 2). Assuming differential expression of these genes reflects, at least in part, the pathogenesis of CFS, the gene contribution to each disease association presumably affects the final phenotype and risk of complications, for example, lymphoma.31,32 It is also interesting that these genomically derived subtypes represent distinct clinical syndromes and that those which were most severe were also those suffering from anxiety / depression, as would be
expected in a disease with a biological basis. Oestrogen receptor signalling is implicated in CFS subtype 7.
Interestingly, it has previously been reported that CFS patients exhibit a downregulation of oestrogen receptor beta.36,37 Oestrogen is an immunomodulator and has multiple effects on the immune system and on other hormones which can themselves affect the immune response.38
Following repeat testing, and confirmation of these findings, it will be important to find a means by which we can determine the subtype of individual CFS patients. For the purpose of subtype diagnosis, use of an 88-gene QPCR-derived signature is cumbersome and so it will be important to determine the most predictive genes within this signature, whose up or downregulation reliably predicts subtype-status. Using this approach, and depending on further research, we may then be able to use a shortlist of 10-20 CFS-associated genes to subtype
individual patients in clinical settings.
We believe these 88 genes to reflect real biological features of these CFS patients, and this is supported by the fact that differential expression of 16 of these genes has been reported previously by our group.22 If these findings are confirmed, there are various options for clinical trials using existing therapies which have been shown to be safe, based on targetting of key genes in patients of different CFS subtypes, namely, IL6ST, TNF, CXCR4, APP, FNTA.
Interestingly, one anti-TNF drug (etanercept) has already been trialled using an 8 week regimen in 6 CFS patients with reported clinical benefit in fatigue and pain in all subjects. Although this was not published as a paper, it was presented by Kristin Lamprecht and colleagues from Minnesota at the International Association for CFS (IACFS) Meeting in Seattle in 2001 (http://www.cfs-news.org/aacfs-01.htm). Unfortunately, this was not followed up because the Peterson group moved out of CFS research around this time (Phil Peterson, personal communication).
In conclusion, we report in detail the genomic and phenotypic differences in 7 genomically defined subtypes of CFS. Further work is required to validate these findings, and this work is underway in our laboratory.10
Acknowledgements
We thank Sir Joseph Hotung for funding of the salaries of JK and BB, and CFS Research Foundation, Hertfordshire, UK for generous funding of this project. There are no competinginterests for any author.
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Figures
Figure 1, panel A
SF36 domain and total scores for each CFS/ME subtype; physical function, physical
role (RP), bodily pain (BP), general health (GH), vitality (VIT), social functioning (SF),
emotional role (RE), mental health (MH) and total score (Total). This figure is reproduced
from Kerr et al, J Infect Dis 2007 (in press).6
Figure 1, panel B
Scores for the clinical questionnaires, Chalder Fatigue Scale (Physical fatigue (P) and Mental
fatigue (M)), McGill pain questionnaire, SPHERE (psychological (P), somatic (S), total/24,
total/68), and Pittsburgh sleep quality index (PSQI).
Figure 1, panel C
Scores indicating occurrence and severity of 11 clinical symptoms and results of
neurocognitive testing for each CFS/ME subtype; headache (HA), sore throat (ST),
swollen glands (GLA), cognitive defect (COG), muscle pain (MP), joint pain (JP),
muscle weakness (MW), postexertional malaise (PEM), sleep problems (SLE),
fainting / dizziness (F/D), gastrointestinal complaints (GI), numbness / tingling (N/T);
Spatial span (SSP), Verbal recognition memory (VRM). This figure is reproduced from Kerr et
al, J Infect Dis 2007 (in press).6
Figure 1, panel
Histogram showing the numbers of CFS/ME patients of each subtype occurring in
each of the 5 geographical locations. This figure is reproduced from Kerr et al, J Infect Dis
2007 (in press).6
Figure 2
Clustering of fold-difference values for patients of each of the subtypes compared with normal
blood donors for 80 genes. Red, upregulation; green, downregulation. This figure is
reproduced from Kerr et al, J Infect Dis 2007 (in press).6
Figure 3, panel A
Combined gene network for CFS subtype 1.
Figure 3, panel B
Combined gene network for CFS subtype 2.
Figure 3, panel C
Combined gene network for CFS subtype 3.
Figure 3, panel D
Combined gene network for CFS subtype 4.
Figure 3, panel E
Combined gene network for CFS subtype 5.
Figure 3, panel F
Combined gene network for CFS subtype.
Figure 3, panel G
Combined gene network for CFS subtype 7.
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14
Additional legend for Figure 3
Networks are stratified by subcellular location. Genes are colored according to expression
levels determined in the present study; red, upregulation; blue; downregulation; color intensity
reflects the magnitude of the fold-difference between CFS/ME and normal subjects.6
Networks were generated using Ingenuity Pathways Analysis (IPA).
Table 1. Fold-difference values for 88 genes in 55 CFS patients; as a group, and in each of seven CFS subtypes. For each subtype,
genes with fold-difference values of =1.5 up- or down-regulated, as compared with normals, were noted and used in further analysis.
Genes without values for the subtypes are those for which there was missing data for one or more subtypes. Shaded boxes indicate
genes targeted by existing drugs, and those CFS subtypes in which there may be a rationale for a trial of a particular drug (see
Results).
PCR*
2-tail P value for
PCR*
Subtype
1
Subtype
2
Subtype
3
Subtype
4
Subtype
5
Subtype
6
Subtype
7
ABCD4* NM_020323 Hs00245340_m1 2.08 0.028 2.04 1.66 1.46 4.48 2.13
ACTR3 NM_005721 Hs00828586_m1 1.42 0.0042 3.77 2.44 1.93 0.93 1.86 0.87 1.76
AKAP10 NM_007202 Hs00183673_m1 1.54 0.0011 2.47 1.96 1.12 0.99 2.27 2.08 1.93
ANAPC11* NM_016476 Hs00212858_m1 3.32 0.00033 4.33 2.26 2.02 5.57 3.27 4.26
ANAPC5 NM_016237 Hs00212120_m1 2.36 0.00016 3.88 3.64 1.69 3.56 3.88 0.83
APP NM_201413 Hs00169098_m1 2.5 4.33E-09 2.45 3.11 1.73 1.77 3.94 2.87 4.16
ARL4C NM_005737 Hs00255039_s1 2.96 8.90E-06 3.50 1.83 4.61 3.89 3.27
ARPC5 NM_005717 Hs00271722_m1 3.23 6.82E-08 9.03 5.54 5.27 2.25 3.59 1.72 5.81
ARSD NM_001669 Hs00534692_m1 1.98 0.001 2.30 2.08 1.27 1.85 2.67
ATP6V1C1 NM_001695 Hs00184625_m1 2.03 0.00029 3.29 3.06 4.74 1.18 2.68 2.47 1.94
BCOR NM_017745 Hs00372369_m1 1.6 0.0098 1.43 1.53 0.84 0.95 3.66 1.96 1.74
BMP2K NM_198892 Hs00214079_m1 1.3 0.014 0.77 1.41 1.17 0.99 1.48 1.54 0.85
BRMS1* NM_015399 Hs00363036_m1 2.68 0.0014 3.16 1.00 1.37 4.35 2.57
CD2BP2* NM_006110 Hs00272036_m1 1.8 5.35E-06 2.19 1.76 2.37 1.34 1.63 2.78 3.45
CD47 NM_198793 Hs00179953_m1 2.2 0.00013 4.37 3.24 1.30 1.24 3.32 1.91 1.62
CEP350 NM_014810 Hs00402774_m1 2.02 0.0048 3.13 6.76 2.28 1.27 2.80 2.03 1.20
CITED2 NM_006079 Hs00366696_m1 2.39 4.45E-06 2.64 0.75 2.16 2.87 2.23
CMTM6 NM_017801 Hs00215083_m1 1.41 0.012 3.84 2.48 2.38 0.84 1.99 0.89 1.63
CREBBP NM_004380 Hs00231733_m1 1.43 0.016 0.93 1.29 1.50 1.21 2.68
CRK NM_016823 Hs00180418_m1 2.51 1.11E-05 3.65 4.39 1.79 2.31 3.63 3.59
CTBP1 NM_001328 Hs00179922_m1 1.45 0.062 0.86 0.94 1.35 2.49
CXCR4 NM_003467 Hs00607978_s1 1.67 7.80E-05 1.12 1.15 2.18 2.41
EBI2 NM_004951 Hs00270639_s1 3.44 0.0012 11.68 2.91 1.47 6.18 4.89 4.38
EGR1 NM_001955 Hs00152928_m1 2.82 0.015 1.18 7.71 0.94 1.73 2.01 2.97 1.92
EGR3 NM_004421 Hs00231780_m1 1.92 0.017 1.14 1.28 2.60 3.46 1.61
EIF2B4* NM_172195 Hs00248984_m1 2.06 0.025 3.68 1.55 1.10 2.86
EIF3S10 NM_003750 Hs00186707_m1 3.58 0.0029 1.98 3.78 5.64 2.60 5.78
EIF4G1* NM_198241 Hs00191933_m1 3.05 0.0033 3.25 8.57 1.50 1.83 3.71 5.84
EIF4G3 NM_003760 Hs00186804_m1 1.67 1.37E-05 3.07 0.94 1.48 2.28 1.72 2.03
ETS1 NM_005238 Hs00901425_m1 2.11 1.00E-05 2.79 3.51 0.98 1.54 1.64 2.00 2.77
FAM126B NM_173822 Hs00545158_m1 1.64 0.0034 4.64 1.80 2.23 0.95 2.79 1.35 1.01
FNTA NM_002027 Hs00357739_m1 2.18 3.82E-06 4.80 3.32 2.08 1.49 2.48 2.22 1.51
GABARAPL1* NM_031412 Hs00744468_s1 5.64 6.10E-05 12.43 13.97 8.31 2.49 5.02 4.75 4.37
GABPA NM_002031 Hs00745591_s1 8.06 3.00E-04 6.59 23.28 15.56 2.92 3.36 9.27 3.44
Downloaded from jcp.bmj.com on 8 December 2007
16
GCN1L1 NM_006836 Hs00412445_m1 2.05 0.00052 2.03 2.00 1.54 1.38 2.41 4.26 3.22
GLTSCR2 NM_015710 Hs00414236_m1 1.24 0.026 1.97 1.85 1.14 0.77 1.30 1.41 2.20
GNAS NM_080425 Hs00255603_m1 1.7 1.09E-07 2.14 3.16 1.73 1.27 1.67 1.46 1.87
GSN* NM_198252 Hs00609276_m1 2.93 0.00017 4.39 7.07 3.51 1.42 3.79 2.36 5.90
GTF2A2 NM_004492 Hs00362112_m1 1.79 0.03 1.42 5.57 1.16 1.65 1.90
HIF1A NM_001530 Hs00153153_m1 0.81 0.016 1.24 1.35 0.86 0.59 1.07
IFNAR1 NM_000629 Hs00265057_m1 1.76 0.00073 3.90 3.22 2.21 1.05 1.64 2.90 1.54
IL10RA* NM_001558 Hs00387004_m1 1.73 9.87E-06 1.27 1.54 0.76 1.31 1.31 2.87 2.29
IL6R NM_000565 Hs00794121_m1 1.19 0.06 5.42 3.11 3.32 2.94 3.10 3.03 4.08
IL6ST NM_002184 Hs00174360_m1 1.8 0.002 2.87 1.52 0.77 1.22 1.85 1.71 5.06
IL7R NM_002185 Hs00233682_m1 0.82 0.032 1.16 1.34 0.98 0.57 1.44 0.66 0.64
JAK1 NM_002227 Hs00233820_m1 1.91 1.86E-08 1.73 3.19 1.26 1.40 1.94 2.12 2.40
KHSRP* NM_003685 Hs00269352_m1 1.67 0.00026 1.43 2.13 1.25 1.17 1.89 2.49 2.73
MAPK9 NM_139070 Hs00177102_m1 1.4 0.045 1.74 2.36 1.08 2.34 2.12
METTL3 NM_019852 Hs00219820_m1 2.06 0.0001 3.94 2.95 0.76 1.38 1.97 3.14 2.11
MRPL23* NM_021134 Hs00221699_m1 2.06 0.001 5.41 2.10 0.70 1.48 1.98 3.04 3.05
MRPS6 NM_032476 Hs00606808_m1 1.53 0.025 2.53 2.59 1.25 1.33 1.62 0.86
MRRF NM_138777 Hs00751845_s1 8.91 0.0004 28.27 38.40 13.24 2.85 5.85 9.96 9.11
MSN** NM_002444 Hs00792607_mH 1.33 0.0016 1.56 2.16 1.76 1.03 1.02 1.19 1.87
MTMR6 NM_004685 Hs00395064_m1 1.71 0.0025 5.07 2.10 2.38 1.23 2.17 0.59 1.89
NFKB1 NM_003998 Hs00231653_m1 1.59 4.04E-05 2.26 1.14 1.34 1.60 2.56
NHLH1 NM_005589 Hs00271582_s1 11.51 7.00E-04 3.87 47.19 20.19 5.34 3.90 15.62 5.32
NR1D2 NM_005126 Hs00233309_m1 2.44 0.00076 3.17 2.45 2.66 1.08 4.03 2.00
NTE* NM_006702 Hs00198648_m1 1.7 0.04 4.40 8.11 0.97 0.94 1.55 1.33 2.24
NUFIP2 NM_020772 Hs00325168_m1 1.5 0.00036 2.05 1.94 0.74 1.02 2.44 1.97 1.45
PAPOLA NM_032632 Hs00413685_m1 1.32 0.00194 1.90 1.93 1.41 0.88 1.99 1.03 1.40
PDCD2* NM_002598 Hs00751277_sH 6.76 0.0096 26.46 24.28 17.55 2.84 3.78 8.81 5.38
PDCD6 NM_013232 Hs00737034_m1 1.74 0.00019 1.99 1.41 1.94 1.47 2.20 2.10 2.62
PEX16* NM_004813 Hs00191337_m1 1.74 0.0034 2.58 2.46 1.15 2.08 2.13 2.33
PGM2 NM_018290 Hs00217619_m1 2.17 1.68E-06 4.35 3.49 2.76 1.28 3.04 2.26 2.41
PIK3R1 NM_181523 Hs00236128_m1 0.68 0.025 1.61 0.50 0.25 0.46 1.33
PKN1* NM_213560 Hs00177028_m1 1.56 9.40E-05 2.30 2.11 0.95 1.14 1.97 1.73 2.60
POLR2G* NM_002696 Hs00275738_m1 2.58 0.0078 3.38 3.04 2.79 1.23 2.56 8.07 2.63
PPP2R5C NM_002719 Hs00604902_m1 1.38 0.022 2.59 1.63 1.03 0.95 2.15 1.85 1.14
PRKAA1 NM_006251 Hs01562315_m1 1.72 0.00052 3.27 1.98 1.43 1.36 2.23 1.68 0.97
PRKAR1A NM_002734 Hs00267597_m1 2.63 2.91E-08 4.81 5.67 4.73 1.64 2.69 1.54 4.06
PUM2 NM_015317 Hs00209677_m1 1.39 0.00064 1.35 1.32 1.29 1.36 1.81
RAP2C NM_021183 Hs00221801_m1 2.1 0.015 4.97 2.07 1.11 2.24 3.09 0.53
REPIN1 NM_013400 Hs00274221_s1 3.62 6.00E-06 3.86 7.29 2.66 1.67 1.93 4.76 2.19
RNF141 NM_16422 Hs00212656_m1 2.37 1.62E-06 3.79 2.86 3.16 1.85 3.56 2.26 1.02
SELENBP1 NM_003944 Hs00187625_m1 1.92 0.002 3.87 1.86 1.28 2.48 2.31
SFXN1 NM_022754 Hs00224259_m1 1.6 0.022 4.86 1.89 1.57 1.21 3.50 0.96 1.41
SHPRH NM_173082 Hs00542737_m1 1.77 0.05 0.92 2.85 0.98 2.21 1.71 1.23
SNAP23 NM_003825 Hs00187075_m1 2.02 0.00018 5.60 1.82 3.11 1.32 3.37 2.02 1.99
SORL1 NM_003105 Hs00268342_m1 1.54 4.10E-08 1.31 1.89 1.48 1.26 1.51 1.74 3.09
SOS1 NM_005633 Hs00362308_m1 2.31 0.002 4.49 2.69 1.28 1.34 3.91 3.37 1.46
TAF11 NM_005643 Hs00194573_m1 1.87 0.05 2.32 4.97 0.86 1.23 2.71 1.93 2.60
TCF3 NM_003200 Hs00413032_m1 1.44 0.023 1.44 1.00 1.34 2.99
TDP1 NM_018319 Hs00217832_m1 1.67 0.0099 3.25 1.35 1.53 1.25 1.80 2.60 2.14
Downloaded from jcp.bmj.com on 8 December 2007
17
TNFRSF1A NM_001065 Hs00533560_m1 1.37 0.016 8.40 4.68 5.45 2.99 6.12 6.02
UBTF NM_014233 Hs00610729_g1 2.26 0.024 6.23 2.23 1.29 1.09 4.27
USP38 NM_032557 Hs00261419_m1 1.71 0.0021 4.07 2.08 0.96 1.10 2.03 2.05 2.07
WAPAL NM_015045 Hs00386162_m1 1.69 0.027 3.27 3.64 3.35 1.07 2.00 1.16 0.91
WDR26 NM_025160 Hs00228535_m1 2.62 0.00012 5.62 6.11 2.84 1.80 2.33 1.20 3.19
* Reference no. 6.
on 8 December 2007 Downloaded from jcp.bmj.com
18
Table 2. Disease associations, molecular and cellular functions, physiological systems and canonical pathway associations for
differentially expressed genes in 55 CFS patients; as a group and for each of seven genomically derived subtypes.
Combined* Subtype 1 Subtype 2 Subtype 3 Subtype 4 Subtype 5 Subtype 6 Subtype 7
Diseases Haematological
(22)
Neurological (18) Neurological (21) Neurological (16) Neurological (11) Neurological (21) Neurological (25) Neurological (24)
Immunological
(14)
Hematological (14) Hematological (18) Hematological (10) Hematological (10) Hematological (18) Hematological (20) Hematological (23)
Cancer (31) Cancer (21) Inflammatory (11) Cancer (15) Immunological (7) Immunological (13) Immunological (17) Immunological (17)
Dermatological
(3)
Cardiovascular (6) Cancer (26) Endocrine system
(5)
Cancer (14) Cancer (26) Inflammatory (14) Cancer (27)
Endocrine system
(9)
Inflammatory (6) Immunological (15) Metabolic (4) Organismal injury (8) Inflammatory (26) Cancer (26) Inflammatory (12)
Molecular &
Cellular functions
Cellular
development (26)
Cell signalling (26) Cell signalling (31) Gene expression
(16)
Cellular
development (10)
Cell death (30) Cell signalling (33) Cell signalling (33)
Cell death (33) Cellular assembly &
organisation (11)
Cellular growth &
proliferation (24)
Cell death (15) Cell death (13) Cell death (25) Cellular growth and
proliferation (26)
Cell death (27)
Gene expression
(31)
Cellular compromise
(6)
Gene expression
(27)
Cell signalling (16) Cell signalling (14) Cellular movement
(17)
Cellular
development (20)
Cellular movement
(20)
Cellular growth
and proliferation
(31)
Cell death (20) Cellular movement
(18)
Cellular movement
(10)
Cellular growth &
proliferation (12)
Gene expression (25) Gene expression
(27)
Cellular development
(21)
Cellular assembly
and organisation
(15)
Cellular growth &
proliferation (18)
Cell death (25) Protein synthesis (6) Gene expression
(10)
Cellular development
(18)
Cell death (25) Gene expression (27)
Physiological
system
development and
function
Haematological
system
development and
function (22)
Nervous system
development and
function (15)
Immune response
(17)
Hematological
system development
and function (7)
Hematological
system development
and function (8)
Organismal survival
(13)
Hematological
system development
and function (17)
Tissue morphology
(18)
Immune and
lymphatic system
development and
function (18)
Organismal survival
(9)
Organismal survival
(12)
Immune response
(8)
Tissue morphology
(10)
Tissue morphology
(14)
Immune and
lymphatic system
development and
function (16)
Haematological
system development
and function (16)
Tissue
morphology (18)
Haematological
system development
and function (11)
Hematological
system development
& function (15)
Organismal survival
(8)
Organismal survival
(9)
Hematological system
development and
function (16)
Tissue morphology
(16)
Immune and
lymphatic system
development and
function (16)
Organismal
survival (17)
Immune response
(13)
Tissue Morphology
(13)
Tissue morphology
(9)
Immune & lymphatic
system development
and function (6)
Immune and
lymphatic system
development and
function (13)
Organismal survival
(15)
Organismal survival
(16)
Immune response Tissue Morphology Nervous system Nervous system Immune response Immune response Immune response Immune response
(20) (9) development &
function (15)
development and
function (12)
(8) (17) (19) (19)
Top canonical
pathways
Interferon
signalling (2)
Actin cytoskeleton
signalling (6)
IL-6 signalling (5) Axonal guidance
signalling (5)
Insulin receptor
signalling (3)
Acute phase
response signalling
(6)
Acute phase
response signalling
(6)
Ephrin receptor
signalling (5)
IL-6 signalling (6) Ephrin receptor
signalling (5)
Actin cytoskeleton
signalling (6)
Actin cytoskeleton
signalling (4)
B cell receptor
signalling (3)
IL-6 signalling (5) IL-6 signalling (5) Axonal guidance
signalling (6)
EGF signalling (3) ERK/MAPK signalling
(5)
Acute phase
response signalling
(5)
Hepatic fibrosis /
stellate cell
activation (3)
Acute phase
response signalling
(3)
ERK/MAPK signalling
(6)
ERK/MAPK
signalling (6)
Estrogen receptor
signalling (4)
IL-2 signalling (3) IL-6 signalling (4) Ephrin receptor
signalling (5)
Insulin receptor
signalling (3)
ERK/MAPK
signalling (3)
Axonal guidance
signalling (7)
Insulin receptor
signalling (5)
Actin cytoskeleton
signalling (5)
PPAR signalling
(5)
Axonal guidance
signalling (6)
ERK/MAPK
signalling (5)
Acute phase
response signalling
Actin cytoskeleton
signalling (3)
Actin cytoskeleton
signalling (6)
B cell receptor
signalling (5)
Hepatic fibrosis and
stellate cell activation
(4)
*Reference no. 6.
Jonathan R Kerr,1,2 Beverley Burke,1* Robert Petty,1* John Gough,1,2 David Fear,3 Derek L
Mattey,4 John S Axford,1,2 Angus G Dalgleish,1 David J Nutt.5
1Department of Cellular & Molecular Medicine, St George’s University of London, London,UK; Sir Joseph Hotung Centre for Musculoskeletal Disorders; 3Dept of Asthma, Allergy and
Respiratory Sciences, King’s College London, London, UK; 4Staffordshire Rheumatology Centre, Stoke on Trent, UK; 5Psychopharmacology Unit, Dept of Community Based Medicine,
University of Bristol, Bristol, UK.
*Robert Petty and Beverley Burke made equal contributions to this paper.
JCP Online First, published on December 5, 2007 as 10.1136/jcp.2007.053553
Abstract
Chronic Fatigue Syndrome / myalgic encephalomyelitis (CFS/ME) is a multisystem disease,the pathogenesis of which remains undetermined. We have recently reported a study of gene expression which identified differential expression of 88 human genes in patients with CFS/ME. Clustering of QPCR data from CFS/ME patients revealed 7 distinct subtypes with distinct differences in SF-36 scores, clinical phenotypes and severity. In this study, for each CFS/ME subtype, we determined those genes whose expression differed significantly from that of normal blood donors, and then determined gene interactions, disease associations and molecular and cellular functions of those gene sets. Genomic analysis was then related
to clinical data for each CFS/ME subtype. Genomic analysis revealed some common (neurological, haematological, cancer) and some distinct (metabolic, endocrine,
cardiovascular, immunological, inflammatory) disease associations among the subtypes. Subtypes 1, 2 and 7 were the most severe, and subtype 3 was the mildest. Clinical features of each subtype were as follows: subtype 1 (cognitive, musculoskeletal, sleep, anxiety / depression); subtype 2 (musculoskeletal, pain, anxiety / depression); subtype 3 (mild); subtype 4 (cognitive); subtype 5 (musculoskeletal, gastrointestinal); subtype 6 (postexertional); subtype 7 (pain, infectious, musculoskeletal, sleep, neurological, gastrointestinal, neurocognitive, anxiety / depression). It is particularly interesting that in these genomically derived subtypes, there were distinct clinical syndromes and that those which
were most severe were also those with anxiety / depression, as would be expected in a disease with a biological basis.
Introduction
Chronic Fatigue Syndrome / Myalgic Encephalomyelitis (CFS/ME) is a disease characterised by severe and debilitating fatigue, sleep abnormalities, impaired memory and concentration,and musculoskeletal pain.1 In the Western world, the population prevalence is estimated to be
of the order of 0.5%.2,3 Research studies have identified various features relevant to the pathogenesis of CFS/ME such as viral infection, immune abnormalities and immune
activation, exposure to toxins, chemicals and pesticides, stress, hypotension, lymphocyte abnormalities and neuroendocrine dysfunction. However, the precise underlying disease mechanisms and means by which these abnormalities inter-relate in CFS/ME patients, remain to be clarified.4,5
We have previously described a study of gene expression in peripheral blood from 25 CFS/ME patients diagnosed according to the Centers for Disease Control (CDC) diagnostic criteria and 50 normal blood donors using the Affymetrix U133+2 microarray. Genes showing differential expression were further analysed using quantitative PCR (QPCR) in 55 CFS/ME patients and 75 normal blood donors. Differential expression was confirmed for 88 genes, 85
of which were upregulated and 3 downregulated. Highly represented functions were haematological disease and function, immunological disease and function, cancer, cell death,immune response and infection. Clustering of QPCR data from CFS/ME patients revealed 7 distinct subtypes with distinct differences in SF-36 scores, clinical phenotypes and severity.6
In this study, we have determined for each CFS subtype, the fold-difference of each of the 88 CFS-associated genes compared with normal persons. Using a fold-difference cut-off of =1.5, we have then determined those genes which are differentially expressed in each CFS subtype. For each subtype, we report respective gene functions / pathways gene interactions, and disease associations, and relate these to the clinical phenotype details of each.4
Methods
Subjects and clinical characterisation Analyses in this paper are based upon clinical and genomic data from CFS/ME patients whose blood was used for QPCR confirmation of microarray data, as previously reported.6 In total, 55 such patients were enrolled from clinics in Dorset, UK; Bristol, UK; London, UK; and New York City, USA (one patient from Leicester, UK, was managed by a clinic in London). These cases were diagnosed according to the CDC diagnostic criteria for CFS/ME.1 Patients with psychiatric disease were excluded using the Minnesota International Neuropsychiatric Interview (MINI), thus ensuring that none of our patients was suffering from major psychiatric
disease or abuse of alcohol or other drugs. In addition, patients who smoked in the previous year, or were currently taking (or were within 3 months of taking) antibiotics, steroids or antidepressants were excluded from the study.
For all enrolled subjects, according to the recommendations of the International CFS Study
Group,7 severity of physical and mental fatigue was assessed using the Chalder Fatigue Scale;8 level of disability was assessed using the Medical Outcomes Survey Short Form-36 (SF-36); accompanying symptoms were characterised using the Somatic and Psychological
Health Report (SPHERE); sleep abnormalities were assessed using the Pittsburgh Sleep Questionnaire; and assessment of type and severity of pain was performed using the McGill
Pain Questionnaire. For the CFS/ME patients, neurocognitive testing was performed using the Spatial Span (SSP) and Verbal Recognition Memory (VRM) modules of the Cantab software (Cambridge Cognition, UK), which showed abnormal results in CFS/ME.6,9 For each CFS patient, the severity of particular symptoms and level of function was taken from the above questionnaires.
Then for each CFS subtype which was derived by clustering of QPCR data as previously described,6 mean values for each symptom and score were calculated and compared between the subtypes. Analysis of variance (ANOVA) was used to determine the significance of differences in individual SF-36 domain scores between CFS subtypes.
Patients and controls gave written consent according to guidance of the Wandsworth Research Ethics Committee (approval number 05/Q0803/137). For the New York patients,
approval of the local Institutional Review Board was obtained. The human experimentation guidelines of the US Department of Health and Human Services were followed in this study.
Determination of differential expression of human genes in each CFS/ME subtype The threshold cycle (Ct) for each test gene in each sample was compared to a calibrator sample to calculate a ?Ct value. ?Ct values were then normalised to the Ct value for an endogenous control gene, lyceraldehyde-3-phosphate dehydrogenase (GAPDH) in respective samples to give the ??Ct values. Relative quantities (RQ) (2-??Ct) of each mRNA of interest were then calculated. Samples showing a difference between minimum and maximum RQ values of =100 (indicating poor replicate concordance) were excluded. The ttest was used to compare RQ values for the CFS/ME patients with those of the controls. Genes whose mean RQ values differed between the groups (at P=0.05) were included in our CFS/ME-associated gene signature.6 RQ values for all 88 CFS/ME-associated genes were
were normalised and clustered using Genesis software.10
For each CFS subtype, mean relative quantity (RQ) values were calculated. Then, for each gene, the mean RQ value for each CFS subtype was divided by the mean RQ value of the normal blood donors, to provide fold-difference values for each CFS subtype. For each subtype, genes were included for analysis assuming they showed fold-difference values (mean RQ in CFS subtype / mean RQ in Normal) in QPCR experiments of =1.5. Thus an 5 individual gene list was generated for each CFS subtype within the 88 gene signature for CFS. Mean fold-difference values were clustered using Cluster version 2.11 software (without
normalization) and visualised using Treeview version 1.60 software.11
Analysis of gene function and interaction in each CFS/ME subtype
Each of these subtype-specific gene lists was analysed for gene function and interaction using Ingenuity Pathways Analysis (IPA) software (Ingenuity, Redwood City, CA, USA) in order to link CFS/ME-subtype-associated genes into networks based on recognised interactions, and to discern the top associated diseases and disorders, molecular and cellular functions, associated physiological system development and function and canonical pathways. 6
Results
Subjects and clinical characterisation Clinical and genomic data from a total of 55 CFS/ME patients fulfilling CDC diagnostic criteria were used for this study. Of these, 19 were male, and 36 were female, with an overall mean age of 41.6 years and a mean duration of disease of 3.2 years. Additional clinical details are provided elsewhere.6 This study included several CFS/ME patients whose disease was severe and necessitated bed rest for much of the day, and patients who were able to attend
an out-patient clinic. Normal blood donors were used as a comparison group and clinical data for these is available elsewhere.6
Genomic CFS/ME subtypes.
AS previously reported, clustering of QPCR data revealed the presence of 7 genomic CFS subtypes with distinct profiles of gene expression within the 88-gene CFS gene signature.6 Fold-difference values (mean RQ in CFS / mean RQ in normal) for all CFS patients and for each CFS subtype are shown in Table 1. For each subtype, genes with fold-difference values of =1.5 were noted and used in further analysis. This resulted in the following numbers of
differentially-expressed genes in each subtype; 58 (CFS subtype 1), 70 (CFS subtype 2), 48 (CFS subtype 3), 27 (CFS subtype 4), 66 (CFS subtype 5), 69 (CFS subtype 6), 71 (CFS subtype 7), respectively. In table 2, genes without values are those for which there was missing data for particular subtypes.
Analysis of gene function in each CFS/ME subtype
Using Ingenuity Pathways Analysis (IPA) software (Ingenuity, Redwood City, CA, USA), the gene list for each CFS subtype was analysed to determine the most important associated diseases and disorders, molecular and cellular functions, associated physiological system development and function and canonical pathways. The results of this analysis are shown in Table 2. As regards disease associations, analysis revealed some common (neurological,
haematological, cancer) and some distinct (metabolic, endocrine, cardiovascular, immunological, inflammatory) disease associations among the subtypes.
This was also true for cellular and molecular functions, and physiological system development and function analyses (Table 2). As regards the canonical pathways implicated in each subtype, there was more variation between subtypes than for the previous analyses, probably because these assignments are based on fewer genes per pathway. IL-6 signalling was implicated in subtypes 1, 2, 5, 6; B cell receptor signaling was implicated in subtypes 4, 6; oestrogen receptor signaling was implicated in subtypes 7; ephrin receptor signaling was implicated in subtypes 1, 2 and 7; and insulin receptor signaling was implicated in subtypes 3, 4 and 6 (Table 2).
Analysis of gene interaction in each CFS/ME subtype
Gene interaction was assessed for each subtype using IPA software. For each subtype, this analysis generated between 2 and 5 large networks (arbitrarily defined as containing 8 or more CFS-associated genes) based on published gene interactions (data not shown) and a
variable number of smaller networks and single genes for which interactions were not known. For each subtype, all networks, large and small, were combined into a single network, indicating genes found to be upregulated and downregulated and then stratified to show the
subcellular location of each (Figure 3, panels A-G).
Clinical features of each CFS subtype Numbers of patients, mean age and male:female ratio for each subtype were as follows: subtype 1 (2; 27 years; 1:1), subtype 2 (5; 49 years; 4:1), subtype 3 (2; 32 years; 0:2), subtype 4 (19; 44.3 years; 8:11), subtype 5 (7; 51 years; 0:7), subtype 6 (14; 41.1 years; 6:8), subtype 7 (3; 47 years; 0:3). Subtypes 3, 5 and 7 were made up of females only; subtype 2
was predominantly male and the remainder were mixed; age differences were less clearly demarcated. Mean questionnaire scores for each subtype are shown in Figure 1, panels A and B. Clinical symptom severity for each subtype is shown in Figure 1, panel C. CFS subtypes 1 and 7
were the most severe, followed sequentially by subtypes 2, 4, 5 and 6 / 3.
Analysis of variance testing revealed significant differences between groups for the SF36 total score (p = 0.016), social functioning (p = 0.03), and emotional role (p = 0.003), while the difference between groups approached significance for general health (p = 0.08) and mental health (p = 0.08). After adjusting for multiple comparisons significant associations were found between
specific groups and clinical phenotypes. Subtype 7 had most pain, lowest SF-36 scores (along with subtype 1), most severe individual symptoms including swollen glands, sore throat, headaches, etc; subtype 1 had the worst cognition and mental health score, and poor sleep despite having the least pain; subtype 4 had moderate neurocognitive function and cognitive defects combined with moderate levels of bodily pain and sleep problems; subtype 5 had the best mental health but poor neurocognitive function, gastrointestinal complaints and
the most marked muscle weakness and postexertional malaise; and subtype 2 had marked postexertional malaise, muscle pain and joint pain but poor mental health (Figure 1, panels A, B, C).
Summary clinical features of each subtype were as follows: subtype 1 (cognitive, musculoskeletal, sleep, anxiety / depression); subtype 2 (musculoskeletal, pain, anxiety /
depression); subtype 3 (mild); subtype 4 (cognitive); subtype 5 (musculoskeletal, gastrointestinal); subtype 6 (postexertional); subtype 7 (pain, infectious, musculoskeletal, sleep, neurological, gastrointestinal, neurocognitive, anxiety / depression). It is particularly
interesting that in these genomically derived subtypes, there were distinct clinical syndromes and that those which were most severe were also those with anxiety / depression, as would be expected in a disease with a biological basis.
As regards subtype associations with geographical location, subtypes 4 and 6 were predominant in Dorset; subtype 4 was predominant in London and New York, and subtype 5 was predominant in Bristol (Figure 1, panel D).
CFS/ME associated genes which are specifically targeted by existing drugs Within the CFS gene signature, there were 5 human genes which are known to be targeted by one or more existing drugs which are designed or intended for use in other diseases. Based on the expression levels of these 5 genes, these drugs may be predicted to be beneficial for particular CFS subtypes. These genes, corresponding drugs and CFS subtypes are as follows: APP (AAB-001; subtypes 1, 2, 3, 4, 5, 6, 7); CXCR4 (JM1300; subtypes 5, 6); FNTA (lonafarnib, tipifarnib; subtypes 1, 2, 3, 5, 6); IL6ST (tocilizumab; subtypes 1, 2, 5, 6, 7); TNF (golimumab, adalimumab, etanercept, certolizumab pegol, infliximab; subtype 2).
Discussion
This study follows our paper describing differential expression of 88 human genes in CFS patients6 and its purpose is to expand upon the brief description of the genomic and phenotypic aspects of the CFS subtypes given in this earlier paper. It has long been recognized that subtypes of CFS/ME exist, and it has been believed that
these subtypes may, at least in part, reflect particular aetiological factors.12 A symptom-based approach has had some success in identifying musculoskeletal, inflammatory and neurological subtypes,13 however, these groups had only minor differences in overall functional severity in contrast to those of the present study.
It is intriguing that within our 88 gene signature, there are several genes with links to various aetiological triggering factors. For example, virus infection (EIF4G1, EBI2) and organophosphate exposure (Neuropathy Target Esterase (NTE)). EIF4G1 is an eukaryotic translation initiation factor which is bound and cleaved by a range of viruses, including enteroviruses, which both trigger and persistently infect CFS patients.14,15 Whistler and
colleagues have also reported upregulation of EIF4G1 transcript variant 5 (the same variant as we report) in patients with CFS who have rapid (? triggered by virus infection) as compared with insidious onset.16 EIF4G1 is a component of the protein complex, EIF4F, which is crucial in translation.17 These viruses divert EIF4G1 from its utilisation by the cellular machinery to facilitate production of viral proteins.17 EIF4G1 is upregulated in CFS subtypes 1, 2, 3, 4, 6, 7 (Table 1; Figures 2 & 4, panels A, B, C, D, F, G).
Various CFS-associated genes identified have previously been shown to be upregulated in EBV infection, namely NFKB1, EGR1, ETS1, GABPA, CREBBP, CXCR4, EBI2, HIF1A,
JAK1, IL6R, IL7R, PIK3R1. This is very interesting as EBV is a recognized trigger of CFS and is known to reactivate upon stress.18 However, it is difficult to draw conclusions as to the interrelationship of these genes in the different subtypes (Figure 2). The EBV transcription factor BRLF1 was found to be over-represented in the original CFS gene signature, however, this was not tested by PCR.6 The EBV genes, BRLF1 and BZLF1, mediate the switch from latent to lytic phases of EBV infection and during this process they transactivate many human
genes. It is interesting that the BRLF1 gene was identified as being over-represented in the transcription factor analysis, and that specific IgG to the Zebra protein (BZLF1 gene product) has been reported previously in CFS/ME patients.19 EBI2 is a gene which is upregulated 200-fold in EBV infected cells20 and is upregulated in
subtypes 2, 3, 5, 6 and 7, but in none of the normal controls.6
One subject with EBI2 upregulation was a 26 year old female whose CFS had been triggered by laboratorydocumented
EBV infection and who had a chronic course with detectable EBV replication in blood for several years after the acute phase. This suggests the possibility that EBI2 may be
a surrogate marker for ongoing EBV replication in CFS patients, although this remains to be clarified. If this is true, then this would be very useful to inform the decision as to which CFS patients should be treated with valganciclovir, which has been shown to be beneficial in
CFS.21
Three patients had markedly raised levels of NTE, while all normal controls had uniformly low levels; CFS subtypes with significantly raised NTE levels were 1, 2, 5 and 7, of which subtypes 1, 2 and 7 were the most severely affected subtypes. We have previously documented upregulation of NTE in CFS.22 NTE is the primary site of action of organophosphate (OP) compounds, such as sarin, which cause axonal degeneration and paralysis resulting from inactivation of its serine esterase activity23 and in the adult chicken nervous system, OP-modified NTE initiates neurodegeneration. Exposure to OP compounds
may trigger CFS/ME24 and Gulf War Illness (GWI).25
IL10RA is a gene which is critical for T cell activation and immune system homeostasis as polymorphisms in it have been shown to be associated with development of lymphoma,
COPD, autoimmunity, severity of hepatitis C infection, and multiple sclerosis.26-30 In the present study it was upregulated,6 although we have previously found it to be downregulated in CFS patients.22
There were more subjects in our pilot study19 that were bed-bound than in the present study,6 and IL10RA levels appear to be a marker of severity in CFS (as they are
closely correlated with SF-36 general health score), with lower levels reflecting increasing severity (data not shown). It is interesting that disease associations identified in the various subtypes are mostly those
which are already recognised in CFS. However, for any one disease association, there are important variations between the subtypes. For example, for ‘neurological disease’, which applies to all subtypes, the number of genes in this category varies from subtype to subtype
(Table 2). Assuming differential expression of these genes reflects, at least in part, the pathogenesis of CFS, the gene contribution to each disease association presumably affects the final phenotype and risk of complications, for example, lymphoma.31,32 It is also interesting that these genomically derived subtypes represent distinct clinical syndromes and that those which were most severe were also those suffering from anxiety / depression, as would be
expected in a disease with a biological basis. Oestrogen receptor signalling is implicated in CFS subtype 7.
Interestingly, it has previously been reported that CFS patients exhibit a downregulation of oestrogen receptor beta.36,37 Oestrogen is an immunomodulator and has multiple effects on the immune system and on other hormones which can themselves affect the immune response.38
Following repeat testing, and confirmation of these findings, it will be important to find a means by which we can determine the subtype of individual CFS patients. For the purpose of subtype diagnosis, use of an 88-gene QPCR-derived signature is cumbersome and so it will be important to determine the most predictive genes within this signature, whose up or downregulation reliably predicts subtype-status. Using this approach, and depending on further research, we may then be able to use a shortlist of 10-20 CFS-associated genes to subtype
individual patients in clinical settings.
We believe these 88 genes to reflect real biological features of these CFS patients, and this is supported by the fact that differential expression of 16 of these genes has been reported previously by our group.22 If these findings are confirmed, there are various options for clinical trials using existing therapies which have been shown to be safe, based on targetting of key genes in patients of different CFS subtypes, namely, IL6ST, TNF, CXCR4, APP, FNTA.
Interestingly, one anti-TNF drug (etanercept) has already been trialled using an 8 week regimen in 6 CFS patients with reported clinical benefit in fatigue and pain in all subjects. Although this was not published as a paper, it was presented by Kristin Lamprecht and colleagues from Minnesota at the International Association for CFS (IACFS) Meeting in Seattle in 2001 (http://www.cfs-news.org/aacfs-01.htm). Unfortunately, this was not followed up because the Peterson group moved out of CFS research around this time (Phil Peterson, personal communication).
In conclusion, we report in detail the genomic and phenotypic differences in 7 genomically defined subtypes of CFS. Further work is required to validate these findings, and this work is underway in our laboratory.10
Acknowledgements
We thank Sir Joseph Hotung for funding of the salaries of JK and BB, and CFS Research Foundation, Hertfordshire, UK for generous funding of this project. There are no competinginterests for any author.
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Figures
Figure 1, panel A
SF36 domain and total scores for each CFS/ME subtype; physical function, physical
role (RP), bodily pain (BP), general health (GH), vitality (VIT), social functioning (SF),
emotional role (RE), mental health (MH) and total score (Total). This figure is reproduced
from Kerr et al, J Infect Dis 2007 (in press).6
Figure 1, panel B
Scores for the clinical questionnaires, Chalder Fatigue Scale (Physical fatigue (P) and Mental
fatigue (M)), McGill pain questionnaire, SPHERE (psychological (P), somatic (S), total/24,
total/68), and Pittsburgh sleep quality index (PSQI).
Figure 1, panel C
Scores indicating occurrence and severity of 11 clinical symptoms and results of
neurocognitive testing for each CFS/ME subtype; headache (HA), sore throat (ST),
swollen glands (GLA), cognitive defect (COG), muscle pain (MP), joint pain (JP),
muscle weakness (MW), postexertional malaise (PEM), sleep problems (SLE),
fainting / dizziness (F/D), gastrointestinal complaints (GI), numbness / tingling (N/T);
Spatial span (SSP), Verbal recognition memory (VRM). This figure is reproduced from Kerr et
al, J Infect Dis 2007 (in press).6
Figure 1, panel
Histogram showing the numbers of CFS/ME patients of each subtype occurring in
each of the 5 geographical locations. This figure is reproduced from Kerr et al, J Infect Dis
2007 (in press).6
Figure 2
Clustering of fold-difference values for patients of each of the subtypes compared with normal
blood donors for 80 genes. Red, upregulation; green, downregulation. This figure is
reproduced from Kerr et al, J Infect Dis 2007 (in press).6
Figure 3, panel A
Combined gene network for CFS subtype 1.
Figure 3, panel B
Combined gene network for CFS subtype 2.
Figure 3, panel C
Combined gene network for CFS subtype 3.
Figure 3, panel D
Combined gene network for CFS subtype 4.
Figure 3, panel E
Combined gene network for CFS subtype 5.
Figure 3, panel F
Combined gene network for CFS subtype.
Figure 3, panel G
Combined gene network for CFS subtype 7.
Downloaded from jcp.bmj.com on 8 December 2007
14
Additional legend for Figure 3
Networks are stratified by subcellular location. Genes are colored according to expression
levels determined in the present study; red, upregulation; blue; downregulation; color intensity
reflects the magnitude of the fold-difference between CFS/ME and normal subjects.6
Networks were generated using Ingenuity Pathways Analysis (IPA).
Table 1. Fold-difference values for 88 genes in 55 CFS patients; as a group, and in each of seven CFS subtypes. For each subtype,
genes with fold-difference values of =1.5 up- or down-regulated, as compared with normals, were noted and used in further analysis.
Genes without values for the subtypes are those for which there was missing data for one or more subtypes. Shaded boxes indicate
genes targeted by existing drugs, and those CFS subtypes in which there may be a rationale for a trial of a particular drug (see
Results).
PCR*
2-tail P value for
PCR*
Subtype
1
Subtype
2
Subtype
3
Subtype
4
Subtype
5
Subtype
6
Subtype
7
ABCD4* NM_020323 Hs00245340_m1 2.08 0.028 2.04 1.66 1.46 4.48 2.13
ACTR3 NM_005721 Hs00828586_m1 1.42 0.0042 3.77 2.44 1.93 0.93 1.86 0.87 1.76
AKAP10 NM_007202 Hs00183673_m1 1.54 0.0011 2.47 1.96 1.12 0.99 2.27 2.08 1.93
ANAPC11* NM_016476 Hs00212858_m1 3.32 0.00033 4.33 2.26 2.02 5.57 3.27 4.26
ANAPC5 NM_016237 Hs00212120_m1 2.36 0.00016 3.88 3.64 1.69 3.56 3.88 0.83
APP NM_201413 Hs00169098_m1 2.5 4.33E-09 2.45 3.11 1.73 1.77 3.94 2.87 4.16
ARL4C NM_005737 Hs00255039_s1 2.96 8.90E-06 3.50 1.83 4.61 3.89 3.27
ARPC5 NM_005717 Hs00271722_m1 3.23 6.82E-08 9.03 5.54 5.27 2.25 3.59 1.72 5.81
ARSD NM_001669 Hs00534692_m1 1.98 0.001 2.30 2.08 1.27 1.85 2.67
ATP6V1C1 NM_001695 Hs00184625_m1 2.03 0.00029 3.29 3.06 4.74 1.18 2.68 2.47 1.94
BCOR NM_017745 Hs00372369_m1 1.6 0.0098 1.43 1.53 0.84 0.95 3.66 1.96 1.74
BMP2K NM_198892 Hs00214079_m1 1.3 0.014 0.77 1.41 1.17 0.99 1.48 1.54 0.85
BRMS1* NM_015399 Hs00363036_m1 2.68 0.0014 3.16 1.00 1.37 4.35 2.57
CD2BP2* NM_006110 Hs00272036_m1 1.8 5.35E-06 2.19 1.76 2.37 1.34 1.63 2.78 3.45
CD47 NM_198793 Hs00179953_m1 2.2 0.00013 4.37 3.24 1.30 1.24 3.32 1.91 1.62
CEP350 NM_014810 Hs00402774_m1 2.02 0.0048 3.13 6.76 2.28 1.27 2.80 2.03 1.20
CITED2 NM_006079 Hs00366696_m1 2.39 4.45E-06 2.64 0.75 2.16 2.87 2.23
CMTM6 NM_017801 Hs00215083_m1 1.41 0.012 3.84 2.48 2.38 0.84 1.99 0.89 1.63
CREBBP NM_004380 Hs00231733_m1 1.43 0.016 0.93 1.29 1.50 1.21 2.68
CRK NM_016823 Hs00180418_m1 2.51 1.11E-05 3.65 4.39 1.79 2.31 3.63 3.59
CTBP1 NM_001328 Hs00179922_m1 1.45 0.062 0.86 0.94 1.35 2.49
CXCR4 NM_003467 Hs00607978_s1 1.67 7.80E-05 1.12 1.15 2.18 2.41
EBI2 NM_004951 Hs00270639_s1 3.44 0.0012 11.68 2.91 1.47 6.18 4.89 4.38
EGR1 NM_001955 Hs00152928_m1 2.82 0.015 1.18 7.71 0.94 1.73 2.01 2.97 1.92
EGR3 NM_004421 Hs00231780_m1 1.92 0.017 1.14 1.28 2.60 3.46 1.61
EIF2B4* NM_172195 Hs00248984_m1 2.06 0.025 3.68 1.55 1.10 2.86
EIF3S10 NM_003750 Hs00186707_m1 3.58 0.0029 1.98 3.78 5.64 2.60 5.78
EIF4G1* NM_198241 Hs00191933_m1 3.05 0.0033 3.25 8.57 1.50 1.83 3.71 5.84
EIF4G3 NM_003760 Hs00186804_m1 1.67 1.37E-05 3.07 0.94 1.48 2.28 1.72 2.03
ETS1 NM_005238 Hs00901425_m1 2.11 1.00E-05 2.79 3.51 0.98 1.54 1.64 2.00 2.77
FAM126B NM_173822 Hs00545158_m1 1.64 0.0034 4.64 1.80 2.23 0.95 2.79 1.35 1.01
FNTA NM_002027 Hs00357739_m1 2.18 3.82E-06 4.80 3.32 2.08 1.49 2.48 2.22 1.51
GABARAPL1* NM_031412 Hs00744468_s1 5.64 6.10E-05 12.43 13.97 8.31 2.49 5.02 4.75 4.37
GABPA NM_002031 Hs00745591_s1 8.06 3.00E-04 6.59 23.28 15.56 2.92 3.36 9.27 3.44
Downloaded from jcp.bmj.com on 8 December 2007
16
GCN1L1 NM_006836 Hs00412445_m1 2.05 0.00052 2.03 2.00 1.54 1.38 2.41 4.26 3.22
GLTSCR2 NM_015710 Hs00414236_m1 1.24 0.026 1.97 1.85 1.14 0.77 1.30 1.41 2.20
GNAS NM_080425 Hs00255603_m1 1.7 1.09E-07 2.14 3.16 1.73 1.27 1.67 1.46 1.87
GSN* NM_198252 Hs00609276_m1 2.93 0.00017 4.39 7.07 3.51 1.42 3.79 2.36 5.90
GTF2A2 NM_004492 Hs00362112_m1 1.79 0.03 1.42 5.57 1.16 1.65 1.90
HIF1A NM_001530 Hs00153153_m1 0.81 0.016 1.24 1.35 0.86 0.59 1.07
IFNAR1 NM_000629 Hs00265057_m1 1.76 0.00073 3.90 3.22 2.21 1.05 1.64 2.90 1.54
IL10RA* NM_001558 Hs00387004_m1 1.73 9.87E-06 1.27 1.54 0.76 1.31 1.31 2.87 2.29
IL6R NM_000565 Hs00794121_m1 1.19 0.06 5.42 3.11 3.32 2.94 3.10 3.03 4.08
IL6ST NM_002184 Hs00174360_m1 1.8 0.002 2.87 1.52 0.77 1.22 1.85 1.71 5.06
IL7R NM_002185 Hs00233682_m1 0.82 0.032 1.16 1.34 0.98 0.57 1.44 0.66 0.64
JAK1 NM_002227 Hs00233820_m1 1.91 1.86E-08 1.73 3.19 1.26 1.40 1.94 2.12 2.40
KHSRP* NM_003685 Hs00269352_m1 1.67 0.00026 1.43 2.13 1.25 1.17 1.89 2.49 2.73
MAPK9 NM_139070 Hs00177102_m1 1.4 0.045 1.74 2.36 1.08 2.34 2.12
METTL3 NM_019852 Hs00219820_m1 2.06 0.0001 3.94 2.95 0.76 1.38 1.97 3.14 2.11
MRPL23* NM_021134 Hs00221699_m1 2.06 0.001 5.41 2.10 0.70 1.48 1.98 3.04 3.05
MRPS6 NM_032476 Hs00606808_m1 1.53 0.025 2.53 2.59 1.25 1.33 1.62 0.86
MRRF NM_138777 Hs00751845_s1 8.91 0.0004 28.27 38.40 13.24 2.85 5.85 9.96 9.11
MSN** NM_002444 Hs00792607_mH 1.33 0.0016 1.56 2.16 1.76 1.03 1.02 1.19 1.87
MTMR6 NM_004685 Hs00395064_m1 1.71 0.0025 5.07 2.10 2.38 1.23 2.17 0.59 1.89
NFKB1 NM_003998 Hs00231653_m1 1.59 4.04E-05 2.26 1.14 1.34 1.60 2.56
NHLH1 NM_005589 Hs00271582_s1 11.51 7.00E-04 3.87 47.19 20.19 5.34 3.90 15.62 5.32
NR1D2 NM_005126 Hs00233309_m1 2.44 0.00076 3.17 2.45 2.66 1.08 4.03 2.00
NTE* NM_006702 Hs00198648_m1 1.7 0.04 4.40 8.11 0.97 0.94 1.55 1.33 2.24
NUFIP2 NM_020772 Hs00325168_m1 1.5 0.00036 2.05 1.94 0.74 1.02 2.44 1.97 1.45
PAPOLA NM_032632 Hs00413685_m1 1.32 0.00194 1.90 1.93 1.41 0.88 1.99 1.03 1.40
PDCD2* NM_002598 Hs00751277_sH 6.76 0.0096 26.46 24.28 17.55 2.84 3.78 8.81 5.38
PDCD6 NM_013232 Hs00737034_m1 1.74 0.00019 1.99 1.41 1.94 1.47 2.20 2.10 2.62
PEX16* NM_004813 Hs00191337_m1 1.74 0.0034 2.58 2.46 1.15 2.08 2.13 2.33
PGM2 NM_018290 Hs00217619_m1 2.17 1.68E-06 4.35 3.49 2.76 1.28 3.04 2.26 2.41
PIK3R1 NM_181523 Hs00236128_m1 0.68 0.025 1.61 0.50 0.25 0.46 1.33
PKN1* NM_213560 Hs00177028_m1 1.56 9.40E-05 2.30 2.11 0.95 1.14 1.97 1.73 2.60
POLR2G* NM_002696 Hs00275738_m1 2.58 0.0078 3.38 3.04 2.79 1.23 2.56 8.07 2.63
PPP2R5C NM_002719 Hs00604902_m1 1.38 0.022 2.59 1.63 1.03 0.95 2.15 1.85 1.14
PRKAA1 NM_006251 Hs01562315_m1 1.72 0.00052 3.27 1.98 1.43 1.36 2.23 1.68 0.97
PRKAR1A NM_002734 Hs00267597_m1 2.63 2.91E-08 4.81 5.67 4.73 1.64 2.69 1.54 4.06
PUM2 NM_015317 Hs00209677_m1 1.39 0.00064 1.35 1.32 1.29 1.36 1.81
RAP2C NM_021183 Hs00221801_m1 2.1 0.015 4.97 2.07 1.11 2.24 3.09 0.53
REPIN1 NM_013400 Hs00274221_s1 3.62 6.00E-06 3.86 7.29 2.66 1.67 1.93 4.76 2.19
RNF141 NM_16422 Hs00212656_m1 2.37 1.62E-06 3.79 2.86 3.16 1.85 3.56 2.26 1.02
SELENBP1 NM_003944 Hs00187625_m1 1.92 0.002 3.87 1.86 1.28 2.48 2.31
SFXN1 NM_022754 Hs00224259_m1 1.6 0.022 4.86 1.89 1.57 1.21 3.50 0.96 1.41
SHPRH NM_173082 Hs00542737_m1 1.77 0.05 0.92 2.85 0.98 2.21 1.71 1.23
SNAP23 NM_003825 Hs00187075_m1 2.02 0.00018 5.60 1.82 3.11 1.32 3.37 2.02 1.99
SORL1 NM_003105 Hs00268342_m1 1.54 4.10E-08 1.31 1.89 1.48 1.26 1.51 1.74 3.09
SOS1 NM_005633 Hs00362308_m1 2.31 0.002 4.49 2.69 1.28 1.34 3.91 3.37 1.46
TAF11 NM_005643 Hs00194573_m1 1.87 0.05 2.32 4.97 0.86 1.23 2.71 1.93 2.60
TCF3 NM_003200 Hs00413032_m1 1.44 0.023 1.44 1.00 1.34 2.99
TDP1 NM_018319 Hs00217832_m1 1.67 0.0099 3.25 1.35 1.53 1.25 1.80 2.60 2.14
Downloaded from jcp.bmj.com on 8 December 2007
17
TNFRSF1A NM_001065 Hs00533560_m1 1.37 0.016 8.40 4.68 5.45 2.99 6.12 6.02
UBTF NM_014233 Hs00610729_g1 2.26 0.024 6.23 2.23 1.29 1.09 4.27
USP38 NM_032557 Hs00261419_m1 1.71 0.0021 4.07 2.08 0.96 1.10 2.03 2.05 2.07
WAPAL NM_015045 Hs00386162_m1 1.69 0.027 3.27 3.64 3.35 1.07 2.00 1.16 0.91
WDR26 NM_025160 Hs00228535_m1 2.62 0.00012 5.62 6.11 2.84 1.80 2.33 1.20 3.19
* Reference no. 6.
on 8 December 2007 Downloaded from jcp.bmj.com
18
Table 2. Disease associations, molecular and cellular functions, physiological systems and canonical pathway associations for
differentially expressed genes in 55 CFS patients; as a group and for each of seven genomically derived subtypes.
Combined* Subtype 1 Subtype 2 Subtype 3 Subtype 4 Subtype 5 Subtype 6 Subtype 7
Diseases Haematological
(22)
Neurological (18) Neurological (21) Neurological (16) Neurological (11) Neurological (21) Neurological (25) Neurological (24)
Immunological
(14)
Hematological (14) Hematological (18) Hematological (10) Hematological (10) Hematological (18) Hematological (20) Hematological (23)
Cancer (31) Cancer (21) Inflammatory (11) Cancer (15) Immunological (7) Immunological (13) Immunological (17) Immunological (17)
Dermatological
(3)
Cardiovascular (6) Cancer (26) Endocrine system
(5)
Cancer (14) Cancer (26) Inflammatory (14) Cancer (27)
Endocrine system
(9)
Inflammatory (6) Immunological (15) Metabolic (4) Organismal injury (8) Inflammatory (26) Cancer (26) Inflammatory (12)
Molecular &
Cellular functions
Cellular
development (26)
Cell signalling (26) Cell signalling (31) Gene expression
(16)
Cellular
development (10)
Cell death (30) Cell signalling (33) Cell signalling (33)
Cell death (33) Cellular assembly &
organisation (11)
Cellular growth &
proliferation (24)
Cell death (15) Cell death (13) Cell death (25) Cellular growth and
proliferation (26)
Cell death (27)
Gene expression
(31)
Cellular compromise
(6)
Gene expression
(27)
Cell signalling (16) Cell signalling (14) Cellular movement
(17)
Cellular
development (20)
Cellular movement
(20)
Cellular growth
and proliferation
(31)
Cell death (20) Cellular movement
(18)
Cellular movement
(10)
Cellular growth &
proliferation (12)
Gene expression (25) Gene expression
(27)
Cellular development
(21)
Cellular assembly
and organisation
(15)
Cellular growth &
proliferation (18)
Cell death (25) Protein synthesis (6) Gene expression
(10)
Cellular development
(18)
Cell death (25) Gene expression (27)
Physiological
system
development and
function
Haematological
system
development and
function (22)
Nervous system
development and
function (15)
Immune response
(17)
Hematological
system development
and function (7)
Hematological
system development
and function (8)
Organismal survival
(13)
Hematological
system development
and function (17)
Tissue morphology
(18)
Immune and
lymphatic system
development and
function (18)
Organismal survival
(9)
Organismal survival
(12)
Immune response
(8)
Tissue morphology
(10)
Tissue morphology
(14)
Immune and
lymphatic system
development and
function (16)
Haematological
system development
and function (16)
Tissue
morphology (18)
Haematological
system development
and function (11)
Hematological
system development
& function (15)
Organismal survival
(8)
Organismal survival
(9)
Hematological system
development and
function (16)
Tissue morphology
(16)
Immune and
lymphatic system
development and
function (16)
Organismal
survival (17)
Immune response
(13)
Tissue Morphology
(13)
Tissue morphology
(9)
Immune & lymphatic
system development
and function (6)
Immune and
lymphatic system
development and
function (13)
Organismal survival
(15)
Organismal survival
(16)
Immune response Tissue Morphology Nervous system Nervous system Immune response Immune response Immune response Immune response
(20) (9) development &
function (15)
development and
function (12)
(8) (17) (19) (19)
Top canonical
pathways
Interferon
signalling (2)
Actin cytoskeleton
signalling (6)
IL-6 signalling (5) Axonal guidance
signalling (5)
Insulin receptor
signalling (3)
Acute phase
response signalling
(6)
Acute phase
response signalling
(6)
Ephrin receptor
signalling (5)
IL-6 signalling (6) Ephrin receptor
signalling (5)
Actin cytoskeleton
signalling (6)
Actin cytoskeleton
signalling (4)
B cell receptor
signalling (3)
IL-6 signalling (5) IL-6 signalling (5) Axonal guidance
signalling (6)
EGF signalling (3) ERK/MAPK signalling
(5)
Acute phase
response signalling
(5)
Hepatic fibrosis /
stellate cell
activation (3)
Acute phase
response signalling
(3)
ERK/MAPK signalling
(6)
ERK/MAPK
signalling (6)
Estrogen receptor
signalling (4)
IL-2 signalling (3) IL-6 signalling (4) Ephrin receptor
signalling (5)
Insulin receptor
signalling (3)
ERK/MAPK
signalling (3)
Axonal guidance
signalling (7)
Insulin receptor
signalling (5)
Actin cytoskeleton
signalling (5)
PPAR signalling
(5)
Axonal guidance
signalling (6)
ERK/MAPK
signalling (5)
Acute phase
response signalling
Actin cytoskeleton
signalling (3)
Actin cytoskeleton
signalling (6)
B cell receptor
signalling (5)
Hepatic fibrosis and
stellate cell activation
(4)
*Reference no. 6.