WO2007012879A1

WO2007012879A1 - Method, kit and microarray for diagnosing chronic fatigue syndrome (cfs) or myalgic encephalomyelitis (me)

Info

Publication number: WO2007012879A1
Application number: PCT/GB2006/002849
Authority: WO
Inventors: Jonathan Richard Kerr; Stephen Townley Holgate; Selwyn Charles Morgan Richards; Tim Harrison; David Tyrrell
Original assignee: Imperial College Innovations Ltd
Current assignee: Ip2ipo Innovations Ltd
Priority date: 2005-07-29
Filing date: 2006-07-31
Publication date: 2007-02-01
Anticipated expiration: 2008-01-29
Also published as: GB0515685D0

Abstract

The present invention provides methods for diagnosing chronic fatigue syndrome (CFS) or myalgic encephalomyelitis (ME), by measuring the level of expression of one or more genes associated with these conditions.

Description

METHOD, KIT AND MICROARRAY FOR DIAGNOSING CHRONIC FATIGUE SYNDROME (CFS) OR

MYALGIC ENCEPHALOMYELITIS (ME)

The present invention provides a diagnostic test for myalgic encephalomyelitis or Chronic fatigue syndrome.

Chronic fatigue syndrome (CFS) is a multisystem disease, the pathogenesis of which remains undetermined. CFS/myalgic encephalomyelitis (ME) is a disease that is characterised by severe and debilitating fatigue, sleep abnormalities, impaired memory and concentration, and musculoskeletal pain. In the Western world, the population prevalence is estimated to be in the order of 0.5%. Although CFS is now recognised as a genuine clinical entity, a considerable research effort has failed to identify quantifiable parameters that consistently exhibit abnormal results in well documented cases. Therefore, the pathological basis for CFS remains poorly understood. Although there is no known aetiology and no known diagnostic marker, a large number of diverse factors such as viral infection; immune activation; exposure to toxins, chemicals, and pesticides; stress; hypotension; lymphocyte abnormalities; and neuroendocrine dysfunction have been proposed as factors in the pathogenesis of CFS.

Previous reports have studied gene expression in the peripheral blood of patients with CFS. Although there was little agreement between these studies as to the genes identified, only one used quantitative polymerase chain reaction (PCR) to confirm the initial findings and the initial method in this study was differential display. Therefore it is still unclear which genes are potentially involved in CFS.

To address this discrepancy and to investigate the hypothesis that abnormalities of gene regulation occur in CFS, gene expression in peripheral blood mononuclear cells (PBMC) of patients with CFS and normal blood donors was investigated by the inventors using a microarray. Taqman real time PCR was used to confirm those genes identified as being differentially expressed between the groups. Sixteen genes were confirmed as having an expression profile associated with the CFS.

Thus in the first aspect the present invention provides a method of diagnosing chronic fatigue syndrome (CFS) or myalgic encephalomyelitis (ME) comprising:

(a) measuring the level of expression of one or more genes which are differentially expressed in CFS or ME;

(b) comparing the level of expression with that of a normal healthy control.

Genes which are differentially expressed in CFS or ME include ABCD4, PRKCLl, MRPL23, ILlORA, CD2BP2, GSN, NTE, POLR2G, PEX16,

EIF2B4, EIF4G1, ANAPCI l, PDCD2, KHSRP, BRMSl, and GABARAPLl. Preferably the gene is ILlORA.

As used herein "differentially expressed" refers to genes which are either upregulated or downregulated in CFS or ME, compared to normal healthy controls.

Step (a) can be carried out using RT-PCR. RNA can be extracted from a sample obtained from a patient. Suitable primers can be designed that hybridise to those genes known to be expressed differentially in CFS or ME. Pairs of primers are designed to hybridise and thus amplify the genes of interest. Methods for designing such primers, and carrying out RT-PCR are well known to the person skilled in the art. The level of expression for the gene of interest can be determined from the amount of product created during the amplification process.

Alternatively the method can be carried out using a microarray. As used herein the term "microarray" refers to a set of oligonucleotide probes arranged on a solid matrix, such as a microscope slide or silicon wafer. The oligonucleotide probes are 10-50 nucleotides in length, preferably 15-40 nucleotides, more preferably 20-30 nucleotides, most preferably 24 nucleotides. Each probe has a defined locus. The microarray preferably comprises oligonucleotide probes corresponding to those genes known to be differentially expressed in CFS or ME. cRNA derived from a sample obtained from the patient can be labelled and hybridised to the oligonucleotide probes on the microarray to detect the level of gene expresion.

In another aspect the present invention provides the use of a microarray comprising one or more oligonucleotide probes corresponding to those genes which are differentially expressed in CFS or ME in the diagnosis of CFS or ME. Preferably, the microarray comprises or consists of oligonucleotide probes corresponding to the genes ABCD4, PRKCLl, MRPL23, ILlORA, CD2BP2, GSN, NTE, POLR2G, PEX16, EIF2B4, EIF4G1 , ANAPCl 1 , PDCD2, KHSRP, BRMSl, and GABARAPLl.

In a further aspect the present invention provides a microarray comprising or consisting of oligonucleotide probes corresponding to the genes ABCD4, PRKCLl, MRPL23, ILlORA, CD2BP2, GSN, NTE, POLR2G, PEX16,

EIF2B4, EIF4G1, ANAPCI l, PDCD2, KHSRP, BRMSl, and GABARAPLl. Preferably the microarray consists of oligonucleotides corresponding to the genes ABCD4, PRKCLl, MRPL23, ILlORA, CD2BP2, GSN, NTE, POLR2G, PEX16, EIF2B4, EIF4G1, ANAPCIl, PDCD2, KHSRP, BRMSl, and GABARAPLl, and one or more control oligonucleotide probes. Examples of such control oligonucleotide probes include mismatch probes which differ from oligonucleotides which are a perfect match for the desired gene by a single nucleotide. Oligonucleotide probes which allow for internal calibration can also be used as control oligonucleotide probes.

In another aspect the present invention provides a kit for diagnosing CFS or ME comprising one or more pairs of suitable primers to carry out RT-PCR to measure the level of expression of one or more genes which are differentially expressed in CFS or ME patients.

In a further aspect, the invention provides a kit for diagnosing CFS or ME comprising a microarray comprising one or more oligonucleotide probes corresponding to those genes which are differentially expressed in CFS or ME in the diagnosis of CFS or ME.

Preferably, the microarray comprises or consists of oligonucleotide probes corresponding to the genes ABCD4,PRKCL1, MRPL23, ILlORA, CD2BP2, GSN, NTE, POLR2G, PEX16, EIF2B4, EIF4G1, ANAPCIl, PDCD2, KHSRP, BRMS 1 , and GABARAPLl .

The invention will now be described with reference to the following figures:

Figure 1 shows hierarchical clustering experiment of differentially expressed gene profiles among patients with chronic fatigue syndrome (CFS; n = 25) and normal persons (n = 25) identified by analysis in BRB Array Tools. The figure was generated using Genepilot software. Each column represents the expression profile for each of the 35 genes. Each row represents a single gene, with its GenBank accession number to the right hand side of the figure. Coloured pixels represent the magnitude of the response for any gene. Shades of dark grey and light grey represent induction and repression, respectively, relative to the mean value for each respective gene among the normal persons. This figure shows a cluster of 18 subjects (from P29-P9) consisting of predominantly patients with CFS (n = 17) and one normal person, who have a similar profile of expression of these 35 genes. P, patients; C, controls.

Figure 2 (A) Bar chart showing the fold difference in gene expression between test and control groups by microarray (shaded) and real time polymerase chain reaction (solid black) for 16 genes that are differentially expressed in chronic fatigue syndrome (CFS). (B) Bar chart showing the mean relative quantity of mRNA transcripts in test (shaded) and control (solid black) groups for 16 genes that are differentially expressed in CFS. Error bars indicate the standard deviation from the mean in each case. All values for the mean relative quantity mRNA transcript are shown on the left y axis, except those for NTE and EIF4G1, which are shown on the right y axis.

Example 1 Subject enrolment Patients with CFS (n = 25) were enrolled from the Dorset CFS service in South East England. These cases were diagnosed according to the criteria of Fukuda and colleagues. Additional clinical information was recorded and is presented in table 1. This includes measurements of physical and mental fatigue using the Chalder Fatigue Scale and measurements of anxiety and depression using the Hospital Anxiety and Depression scales. None of these patients had undergone previous treatment for psychiatric disorders. Patients were sampled at two time points, six months apart, between which their symptoms did not vary significantly; the first sample from each patient was used for microarray analysis whereas the second was used for real time PCR. This approach provides an additional safeguard against attaching particular importance to genes that may be differentially expressed at a single time point, but not reproducibly.

Normal blood donors were enrolled from the East Dorset division of the National Blood Service (NBS); 25 age and sex matched normal blood donors were used as a comparison group for the microarray part of the study, and 21 normal blood donors (age and sex matched as a group) were used as a comparison group for the real time PCR part of the study. The NBS restricts donors to those who fulfil the following criteria: aged between 17 and 59 years; have not given blood in the previous 16 weeks; are not currently suffering from an infection; are not pregnant; are not currently taking (or within three months of taking) antibiotics, steroids, or antidepressants; have not had hepatitis, jaundice, body piercing, acupuncture, or blood transfusion in the past year; do not have two family members who suffered from Creutzfeldt- Jakob Disease; are not known to be positive for human immunodeficiency virus, hepatitis B or C; are not currently or previously abusers of injecting or body building drugs; are not post-vaccination; and do not suffer a chronic illness including malaria. Patients and controls gave written consent according to the guidance of the ethics committees of both the East Dorset , and the Royal Brompton, Harefield & NHLI.

Sample collection and processing

A 20 ml sample of blood was immediately placed in cell preparation tubes containing density gradient solution and EDTA (BD Biosciences, Manchester, UK). PBMCs were isolated by density gradient centrifugation. Total RNA was extracted using Trizol (Invitrogen, Carlsbad, California, USA), washed in phosphate buffered saline, and the quality and amount confirmed by microspectrophotometry (Nanodrop, Rockland, Delaware, USA). Total RNA samples had an absorbance ratio (A260/280) of 1.85-1.95. A 20 ml sample of peripheral blood is estimated to contain a total of approximately 15xlO⁶ PBMCs and buffy coat from this sample yielded 10-25 mg total RNA, which was shipped to Nimblegen, USA, at room temperature in ethanol, for array testing. Total RNA was converted to double stranded cDNA by the Superscript Choice System (Invitrogen) with an oligo dT primer containing the T7 RNA polymerase promoter sequence : 5'-GGCCAGTGAATTGTAATACGACTCACTATAGGGAGGCGG-T24-3'.

A 15 μg aliquot of total RNA was converted into cDNA. In vitro transcription was used to produce biotin labelled cRNA from cDNA using the Ambion MEGAscript T7 kit (Ambion, Austin, Texas, USA). Before hybridisation, cRNA was fragmented to an average size of 50- 200 bp by incubation in 10OmM potassium acetate, 3OmM magnesium acetate, and 4OmM Tris/acetate at 94^QC for 35 minutes. Fragmentation was checked by gel electrophoresis in 1% agarose.

Microarray design and analysis

A custom microarray was manufactured by Nimblegen (Madison, Wisconsin, USA) using maskless array synthesis (http://www.nimblegen.com). The human genes on this design (n = 9522) were selected from the Homo sapiens entries in the RefSeq collection of sequences as of August 2002. Each gene was compared with all others using the BLAST program to remove redundancies. Ten probe pairs for each target were selected from the 3' 1 kb of each target. Probes were spaced evenly over the length of the target region (<1 kb), so that the exact spacing depended on the length of the target sequence. Each probe was 24 nucleotides in length. For each perfect match probe there was also a mismatch probe, which differed by a single nucleotide. Labelled cRNA was hybridised to the oligonucleotide probes on the microarray. After washing, arrays were stained with streptavidin-cy3 conjugate (Amersham Biosciences, Piscataway, New Jersey, USA) for 25 minutes at room temperature, followed by washing and a blow dry step using high pressure grade 5 Argon (Badger Welding, Madison, Wisconsin, USA). Slides were scanned using a GenePix 4000B microarray scanner (Axon Instruments, Union City, California, USA), and the feature intensities extracted from the TIF files were calculated by the scanner software using a proprietary application developed at NimbleGen (Madison, Wisconsin, USA). This application calculates mean signal intensities for the pixels that define each feature (3x3 grid of pixels). The intensities for each gene are calculated by taking the mean of the intensities for the perfect match probes specific to each target minus the mean of the intensity of the mismatch probes. Probes that differed from the mean for the set by more than 3 SD were removed from the set and the mean recalculated. Average differences (recalculated mean) were used for subsequent analysis.

Data analysis was performed using BRB ArrayTools version 3.02 (Molecular Statistics and Bioinformatics Section, National Cancer Institute, Bethesda, Maryland, USA) developed by Dr R Simon and A Peng (http://linus.nci.nih.gov/BRB-ArrayTools.html). Average difference values were normalised to median over the array. The data were filtered so that only those genes that were adequately measured on 75% of the arrays were included. A class comparison protocol was used to identify genes whose degree of expression differed significantly by > 1.5 fold between the two groups. This consisted of a multivariate permutation test, which was computed based on 1000 random permutations using the following parameters: nominal significance level = 0.001; confidence level of false discovery rate assessment = 50%; maximum allowed number of false positive genes = 10; maximum allowed proportion of false positive genes = 0.1. Values for differentially expressed genes were used to cluster all 50 subjects using Genepilot software (http://www.genepilot. com) (TG Services, El Sobrante, California, USA).

Taqman real time PCR

Taqman real time PCR (Applied Biosystems, Foster City, California, USA) was used to confirm the importance of genes identified by array experiments in the same group of CFS cases (n = 17) and a different group of normal controls (n = 21); the controls were age and sex matched. cDNA was prepared from total RNA using the random hexamer method of reverse transcription, according to the instructions of the kit manufacturer (Applied Biosystems, Warrington, UK). Experiments were performed in triplicate in a custom 384 well low density array format using the ABI PRISM 7900HT instrument (Applied Biosystems) incorporating 38 target gene assays (including three instances where two assays were used for a single gene to include transcript variants) (table 2) along with endogenous controls, namely: HMBS (hydroxymethylbilane synthase), HPRT-I (hypoxanthine phosphoribosyltransf erase 1), GAPDH (glyceraldehyde-3-phosphate dehydrogenase), and eukaryotic 18S rRNA. Fluorogenic probes were 5' labelled with 6-carboxyfluorescein (FAM) and 3' labelled with MGB non- fluorescent quencher. Reactions were performed in a 1 μl reaction volume in Taqman Universal PCR Mastermix (Applied Biosystems UK). For each card channel (48 PCR reactions), 50 ng total RNA from PBMCs converted to cDNA was used as inoculum. Fluorescent signal detection used "ROX" as the internal passive reference dye.

The inoculum (100 μl) was a mixture of Taqman universal PCR master mix (Applied Biosystems) (50 ml), RNase/DNase free sterile water (45 μl), and cDNA (5 μl). Cycling times and temperatures were as follows. Initial denaturation was carried out for 10 minutes at 95°C, followed by 40 cycles of denaturation at 95°C for 15 seconds and combined primer annealing/extension at 60⁰C for one minute. Data was displayed using SDS 2.1 software (Applied Biosystems). GAPDH gave the least variable results in all samples and was used as the endogenous reference control. The threshold cycle (Ct) for each gene/sample pair was compared with a calibrator sample and a DCt value used to calculate a relative quantity of gene expression compared with the calibrator. RQ values were finally normalised to GAPDH expression. The F test for equality of standard deviations was used to compare mean RQ values for each gene in test versus control groups.

RESULTS

CFS disease phenotype

AU 25 patients with CFS were diagnosed according to the Centers for Disease Control criteria of Fukuda et al. Table 1 summarises the patient and clinical details. Our study included patients with CFS whose disease was severe and necessitated bed rest for much of the day, and also several patients whose disease was of a milder nature (Table 1).

Microarray analysis

Analysis of microarray data identified 35 genes that showed significantly different expression in patients with CFS compared with normal controls (Table 2). Hierarchical clustering of subjects on the basis of their degree of expression of these 35 genes revealed a cluster of 18 subjects, comprising 17 CFS patients and one normal person, whose expression profiles were very similar but distinctly different from the other patients and controls (Fig 1). However, this patient cluster did not differ significantly with regard to other clinical variables shown in Table 1. Taqman real time PCR

TaqMan real time PCR analysis was used to confirm the importance of genes that were identified using microarray analysis. Significantly different expression, with the same profile as in gene arrays, was confirmed for 16 of 33 genes in the 17 patients tested. This method revealed upregulation of 15 genes and downregulation of one gene (Table 2). Figure 2 illustrates the concurrent fold difference in gene expression between test and control groups for the 16 differentially expressed genes for both microarray and real time PCR analysis. The standard deviation from the mean is also shown in Figure 2B and provides an indirect measure of the probable reproducibility of differential expression for each gene. In general, the standard deviation of these 16 genes in normal persons is very much lower than in patients with CFS, except for IL-IORA, in which the opposite is the case.

Table 3 provides information on the chromosomal location, expression, subcellular localisation, and function of these 16 genes. Although these genes do not fit neatly into known metabolic pathways, several broad themes are apparent. For example, T cell activation and neuronal and mitochondrial function.

The expression of 16 genes was significantly different in patients compared with controls in both microarray analysis and real time PCR. These genes may be important in the pathogenesis of CFS and can be grouped according to immune, neuronal, mitochondrial, and other functions that have particular relevance to the present knowledge of the epidemiology of CFS (table 4). Our present study has certain parallels with two published studies in this area, summarised in table 4. T cell activation is suggested by upregulation of CD2BP2 and downregulation of IL-IORA; in addition, PRKCLl plays a role in the immune response. Genes that are active in the immune response have been found to be differentially expressed in all studies of gene expression in CFS (table 4). Furthermore, genes that are crucial for T cell activation have also been found to be upregulated in all three studies, namely: CD2BP2 and IL- 10RA (present study); moesin and cathepsin C4; ITGA and NFATC3.5 These findings are consistent with previous work showing that patients with CFS have evidence of immune activation, such as increased numbers of activated T cells and cytotoxic T cells, and raised circulating cytokine concentrations.

A neuronal component is suggested by the upregulation of PRKCLl, NTE, GSN, GABARAPLl, KHSRP, and EIF2B4. Protein kinase C family members are implicated in various psychiatric and affective disorders, and have been implicated in previous gene studies of CFS. NTE is a target for organophosphates and chemical warfare agents, both of which may precipitate CFS, on the basis of a neuropathy resulting from inactivation of serine esterase activity. GSN regulates cell growth and plays a role in amyloidosis (Finnish type), which may result in dysfunction of neurones, skeletal muscle, and thyroid gland. GABARAPLl is a microtubule associated anchor protein with increased expression in neuronal cells. KHSRP facilitates splicing of the Nl exon of the SRC protooncogene in neuronal but not other cells. EIF2B4 is a mitochondrial translation initiation factor and one of the EIF2B family, within which mutations have been shown to be associated with central nervous system hypomyelination and encephalopathy. Upregulation of an EIF2B3 gene homologue (B Q580379) has been reported. These findings are interesting in that abnormalities in the white matter of the frontal lobes have been found in patients with CFS using magnetic resonance imaging and have been suggested to account for the cognitive defect in CFS. Neuronal gene involvement in CFS has also been reported. Mitochondrial involvement is suggested by the upregulation of EIF2B4, EIF4G1 (see above), and MRPL23. Mitochondrial gene upregulation has also been reported. The cell cycle is implicated by upregulation of ANAPCIl, which regulates the onset of anaphase by mediation of degradation of mitotic cyclins. Upregulation of MADlLl, which prevents the onset of anaphase until all chromosomes are aligned at the metaphase plate, has been reported.

Transcriptional perturbation is suggested by the upregulation of POLR2G and BRMSl. The upregulation of genes homologous with POLRlB (BQ580386) and RCOR3 (BQ580388), which are each involved in transcriptional regulation has been reported.

Upregulated peroxisomal function is suggested by the upregulation of ABCD4 and PEX 16, which may suggest enhanced defence to oxidative stress in CFS. Oxidative stress has already been suggested as a disease mechanism in CFS. Persistent virus infection is a recognised feature of CFS, which is interesting in the light of our finding of upregulation of EIF4G1 transcript variant 5, a mitochondrial translation initiation factor. This finding has also been reported in patients with CFS who have rapid (?triggered by virus infection) as compared with insidious onset. EIF4G1 is a component of the protein complex, EIF4F, which is crucial in translation through its involvement in the recognition of the mRNA cap, ATP dependent unwinding of 59 terminal secondary structure, and recruitment of mRNA to the ribosome. Various viruses have developed strategies to divert EIF4G1 from its utilisation by the cellular machinery to facilitate production of viral proteins. The best characterised example is that of poliovirus, but this has also been demonstrated to occur with coxsackie virus, rhinoviruses, rotavirus, influenza virus, adenovirus, vesicular stomatitis virus, and human immunodeficiency virus 1. Therefore, the upregulation of EIF4G1 identified in our present study may represent a common host response to persistent infection with several different viruses. The vulnerability of EIF4G1 to virus modification may have particular importance for the development of CFS after an acute virus infection. In conclusion, we report the differential expression of 16 human genes in patients with CFS compared with normal controls. The involvement of genes from several disparate pathways suggests a complex pathogenesis involving T cell activation and abnormalities of neuronal and mitochondrial function, and suggests possible molecular bases for the recognised contributions of organophosphate exposure and virus infection, respectively.

Table 1 Patient information including summary of aspects used for the CFS case definition and scores for physical fatigue, mental fatigue, anxiety and depression.

Clinical parameter Proportion

Gender 36% male / 64% female Age Mean = 40.6 years

Duration of disease (years) Mean = 2.3 years CFS began with flu-like illness 60% Impaired memory Impaired concentration 84% Sore throat 68% Myalgia 80% Arthralgia 52% Headache 76% Unrefreshing sleep 84% Post-exertional malaise 92% Tender lymphadenopathy 72% Autonomic features 80% Atopic features 52%

Physical fatigue (Chalder) (most severe = 21) Mean = 17.2; Range 12-21 Mental fatigue (Chalder) (most severe = 12) Mean = 9.28; Range 5-12 Anxiety score (HAD) (most severe = 21) Mean = 9.88; Range 3-18 Depression score (HAD) (most severe = 21) Mean = 7.96; Range 1-20 Table 2 Genes showing a significant >1.5 fold change between cases and controls in microarray experiments

GenBank Homo sapiens gene name (mRNA) Gene symbol¹¹ Fold P value Taqman assay ID Fold P value

Accession No. difference difference

(array) (PCR)

NM_020325 ATP-binding cassette, sub-family D (ALD), member 4, tv-4 ABCD4 3.398 0.00257 Hs00245340_ml 1.825 0.0019

NM_002741 Protein kinase C-like 1 PRKCLl 2.288 0.00438 Hs00177028_ml 2.669 1.09 x 10^"5

NM_021134 Mitochondrial ribosomal protein L23 MRPL23 2.175 0.00135 Hs00221699_ml 4.032 1.25 x lO^"6

NM_001558 Interleukin 10 receptor, alpha ILlORA 0.252 0.00225 Hs00387004_ml 0.395 2.34 x lO^"12

NM_006110 CD2 antigen (cytoplasmic tail) binding protein 2 CD2BP2 2.311 0.00158 Hs00272036_ml 2.411 2.6 x 10~⁴

NM_000177 Gelsolin (amyloidosis, Finnish type) GSN 2.141 0.00105 Hs00609276_ml 1.83 6.23 x 10^"7

NMI OO67O2 Neuropathy target esterase NTE 2.877 0.00043 Hs00198648_ml 6.409 1.31 x 10^"12

NM_002696 polymerase (RNA) E (DNA directed) polypeptide G POLR2G 2.264 0.00372 Hs00275738_ml 3.407 3.69 x W⁶

NMI 0O4813 Peroxisomal biogenesis factor 16, tv-1 PEX16 3.004 0.00213 Hs00191337_ml 1.758 0.0126

NM_015636 Eukaryotic translation initiation factor 2B, subunit 4 delta, tv-1 EIF2B4 2.414 0.00125 Hs00248984_ml 1.882 1.8 x 10^"5

NM_004953 Eukaryotic translation initiation factor 4 gamma, 1, tv-5 EIF4G1 3.081 0.00177 HsOO191933_ml 2.964 7.63 x 10^"13

NMJ316476 APCl 1 anaphase promoting complex subunit 11 homolog ANAPCIl 3.278 0.00219 Hs00212858_ml 2.366 3.5 x 10^"δ

NM_002598 Programmed cell death 2, tv-1 PDCD2 2.052 0.00266 Hs00751277_sH 1.887 1.3 x 10^"H

NM_003685 KH-type splicing regulatory protein (FUSE binding protein 2) KHSRP 2.366 0.00139 Hs00269352_ml 1.64 0.0022

NM_015399 Breast cancer metastasis-suppressor 1 BRMSl 2.246 0.00219 Hs00363036_ml 1.598 0.003

NM_031412^B GABA(A) receptor-associated protein like 1 GABARAPLl 2.358 0.00046 Hs00744468_sl 1.838 0.0097

NM_004655 Axin 2 (conductin, axil) AXIN2 0.351 0.00056 Hs00610344_ml 0.780 0.023

NM_019051^A Mitochondrial ribosomal protein L50 MRPL50 0.427 0.00409 Hs00745120_sl 0.815 0.0019

NM_005103^c Fasciculation and elongation protein zeta 1 (zygin I), tv-1 FEZl 0.336 0.00007 Hs00192714_ml 3.485 2.3 x 10^"6

NM_002093 Glycogen synthase kinase 3 beta GSK3B 2.294 0.00238 Hs00275656_ml 0.763 0.0015

NM_005098 Musculin (activated B-cell factor-1) MSC 0.374 0.00121 Hs00231955_ml 1.118 0.036

NM_138325 Paired basic amino acid cleaving system 4, tv-6 PACE4 0.473 0.00414 Hs00159844_ml 2.093 1.2 X lO^"4

NM_003584 Dual specificity phosphatase 11 DUSPIl 0.366 0.00005 Hs00186058_ml 1.725 0.134

NM_002483 Carcinoembryonic antigen-related cell adhesion molecule 6 CEACAM6 0.301 0.00048 Hs00366002_ml 1.585 0.182

NM_019051^A Mitochondrial ribosomal protein L50 MRPL50 0.427 0.00409 Hs00747929_ml 1.136 0.178

NM_030575 Hypothetical protein MGC10334 MGC 10334 2.584 0.00118 Hs00257998_sl 1.724 0.067

NM_031412^B GABA(A) receptor-associated protein like 1 GABARAPLl 2.358 0.00046 Hs00740588_mH 0.93 0.127

NM_007175 Chromosome 8 open reading frame 2 C8orf2 2.554 0.00206 Hs00200360_ml 1.198 0.052

NM_032118 Hypothetical protein FIJ12953 FLJ12953 1.931 0.00309 Hs00259557_ml 1.139 0.203

NM_004879 Etoposide induced 2.4 mRNA EI24 0.45 0.00181 Hs00747550_ml 1.03 0.085

NM_079834 Secretory carrier membrane protein 4 SCAMP-4 2.8 0.00069 Hs00365263_ml 1.586 0.173

NM_022145 Leucine zipper protein FKSG14 FKSG14 0.345 0.00205 Hs00259557_jnl 1.212 0.345

NM_005103^c Fasciculation and elongation protein zeta 1 (zygin I), tv-1 FEZl 0.336 0.00007 Hs00363763_ml 1.062 0.41

NM_003409 Zinc finger protein 161 homolog ZFP161 0.415 0.00146 NA NT NT

NM_007075 JM5 protein JM5 2.383 0.00172 NA NT NT

NM_005345 Heat shock 7OkDa protein IA HSPAlA 3.198 0.00003 NA NT NT

00

NM_017616 Hypothetical protein FLJ20004 FLJ20004 2.679 0.00237 NA NT NT

NM_020663 Ras homolog gene family, member J ARHJ 2.277 0.00346 NA NT NT

Table 3 Chronic fatigue syndrome associated genes: chromosomal location, expression, subcellular localisation, and function

GenBank Homo sapiens gene name (mRNA) Gene symbol* Chromosomal Expression Subcellular Gene Accession location localisation function

No.

NM_020325 ATP-binding cassette, sub-family D (ALD), A ABBCCDD44 14q24.3 Ubiquitous Peroxisomal ALDP member 4, tv-4 membrane expression NM 002741 Protein kinase C-like 1 PRKCLl 19pl3.1-pl2 Ubiquitous Cytoplasm Regulation of cell motility

NM_021134 Mitochondrial ribosomal protein L23 MRPL23 Ilpl5.5 Widely Mitochondri Protein al matrix biosynthesis NM_001558 Interleukin 10 receptor, alpha ELlORA Ilq23.3 - Unknown Cytokine signalling

NM_006110 CD2 antigen (cytoplasmic tail) binding CD2BP2 16pl2.1 - Unknown - protein 2 NM_000177 Gelsolin (amyloidosis, Finnish type) GSN 9q33.3 - Unknown Severing and capping of actin

NM_006702 Neuropathy target esterase NTE 19pl3.3-pl3.2 Nervous Plasma Neurodegene system membrane rative disease

NM_002696 polymerase (RNA) H (DNA directed) POLR2G Ilql3.1 Nervous Nucleus Transcription polypeptide G system from Pol π NM_004813 Peroxisomal biogenesis factor 16, tv-1 PEX16 llpll.ll - Peroxisomal Peroxisomal membrane biogenesis

NM_015636 Eukaryotic translation initiation factor 2B, EIF2B4 2p23.3 Ubiquitous Mitochondri Initiation of subunit 4 delta, tv-1 on translation

NM_004953 Eukaryotic translation initiation factor 4 EIF4G1 3q27-qter Ubiquitous Mitochondri Initiation of gamma, 1, tv-5 on translation NM_016476 APCIl anaphase promoting complex ANAPCIl 17q25.3 Brain, heart, Nucleus Ubiquitin subunit 11 homolog pancreas, ligase lung activity

NM_002598 Programmed cell death 2, tv-1 PDCD2 6q27 Ubiquitous Cytoplasm Apoptosis and regulation of cell proliferation κ>

O

NM_003685 KH-type splicing regulatory protein (FUSE KHSRP 19pl3.3 Nervous Nucleus Neurone- binding protein 2) system specific splicing of the Nl exon of SRC; assembly of other proteins

NM_015399 Breast cancer metastasis-suppressor 1 BRMSl 14ql3.1 Brain Unknown -

NM_031412^z GABA(A) receptor-associated protein like 1 GABARAPLl 12pl2.3 Ubiquitous Unknown

Table 4 Possible mechanisms of disease in CFS based on three gene expression studies, each on patients with CFS and normal controls.

Mechanism Present study Powell and colleagues Vernon and colleagues Microarray (9,522 genes) / Differential display / qPCR Filter array (1764 genes) - 5 qPCR - 25CFS / 25 Normal 7 CFS / 4 Normal CFS - 17 Normal

T-cell activation CD2BP2, IL-IORA, Moesin, CTSC ITGA, NFATC3,

Immune response PRKCLl TNF, MAIL IL-8, CMRF35, ICAM2,

ITGB, IER2, PLA2G2A,

LCP-I₅ PRKCL

Neurone PRKCLl, GSN, KHSRP, HD, PRKCL, ataxin,

NTE, GABARAPLl ADRA2A, DCTNl

Mitochondrion EIF4G1, EIF2B4, MRPL23 SLC25A16, MCFP

Skeletal muscle GSN DCTNl

Thyroid GSN THRAP2, SLC25A16 GDC

Cell cycle ANAPCIl MADlLl

Apoptosis PDCD2

Transcription POLR2G, BRMSl RCOR3, POLRlB

Peroxisome ABCD4, PEX16

Claims

1. A method of diagnosing chronic fatigue syndrome (CFS) or myalgic encephalomyelitis (ME) comprising: (a) measuring the level of expression of one or more genes which are differentially expressed in CFS or ME;

(b) comparing the level of expression with that of a normal healthy control.

2. A method as claimed in claim 1 wherein said gene is selected from ABCD4, PRKCLl, MRPL23, ILlORA, CD2BP2, GSN, NTE, POLR2G, PEX16, EIF2B4, EIF4G1, ANAPCIl, PDCD2, KHSRP, BRMSl, and GABARAPLl.

3. A method as claimed in claim 1 or claim 2 wherein step (a) is carried out using RT-PCR.

4. A method as claimed in claim 1 or claim 2 carried out on a microarray.

5. A method as claimed in claim 4 wherein said microarray comprises or consists of oligonucleotide probes corresponding to the genes ABCD4, PRKCLl, MRPL23, ILlORA, CD2BP2, GSN, NTE, POLR2G, PEX16, EIF2B4, EIF4G1, ANAPCI l, PDCD2, KHSRP, BRMSl, and GABARAPLl.

6. A microarray comprising or consisting of oligonucleotide probes corresponding to the genes ABCD4, PRKCLl, MRPL23, ILlORA, CD2BP2, GSN, NTE, POLR2G, PEX16, EIF2B4, EIF4G1, ANAPCI l, PDCD2, KHSRP, BRMSl, and GABARAPLl.

7. The microarray of claim 6, wherein said microarray consists of oligonucleotides corresponding to the genes ABCD4, PRKCLl, MRPL23, ILlORA, CD2BP2, GSN, NTE, POLR2G, PEX16, EIF2B4, EIF4G1, ANAPCIl, PDCD2, KHSRP, BRMSl, and GABARAPLl, and one or more control oligonucleotide probes.

8. The use of a microarray comprising one or more oligonucleotide probes corresponding to those genes which are differentially expressed in CFS or ME in the diagnosis of CFS or ME.

9. The uses as claimed in claim 8 wherein said microarray is a microarray as defined in claim 6 or claim 7.

10. A kit for diagnosing CFS or ME comprising one or more pairs of suitable primers to carry out RT-PCR to measure the level of expression of one or more genes which are differentially expressed in CFS or ME patients.

11. A kit for diagnosing CFS or ME comprising a microarray comprising one or more oligonucleotide probes corresponding to those genes which are differentially expressed in CFS or ME in the diagnosis of CFS or ME.

12. A kit as claimed in claim 10 or claim 11 wherein said gene is selected from ABCD4, PRKCLl, MRPL23, ILlORA, CD2BP2, GSN, NTE, POLR2G, PEX16, EIF2B4, EIF4G1, ANAPCI l, PDCD2, KHSRP, BRMSl, and GABARAPLl.

13. A kit as claimed in claim 11 comprising a microarray as defined in claim 6 or claim 7.