Chronic Fatigue Syndrome: Whistler T

Vernon SD, Whistler T, Aslakson E, Rajeevan M, Reeves WC. · Center for Infectious Diseases, Division of Viral and Rickettsial Diseases, National Centers for Disease Control and Prevention, Atlanta, GA 30333, USA. · Pharmacogenomics. · Pubmed #16515400 No free full text.

Abstract: Chronic fatigue syndrome (CFS) is prevalent, disabling and costly. Despite extensive literature describing the epidemiology and clinical aspects of CFS, it has been recalcitrant to diagnostic biomarker discovery and therapeutic intervention. This is due to the fact that CFS is a complex illness defined by self-reported symptoms and diagnosed by the exclusion of medical and psychiatric diseases that may explain the symptoms. Studies attempting to dissect the pathophysiology are challenging to design as CFS affects multiple body systems, making the choice of which system to study dependent on an investigators area of expertise. However, the peripheral blood appears to be facilitating the molecular profiling of several diseases, such as CFS, that involve bodywide perturbations that are mediated by the CNS. Successful molecular profiling of CFS will require the integration of genetic, genomic and proteomic data with environmental and behavioral data to define the heterogeneity in order to optimize intervention.

Presson AP, Sobel EM, Papp JC, Suarez CJ, Whistler T, Rajeevan MS, Vernon SD, Horvath S. · Biostatistics, University of California, Los Angeles, CA, USA. · BMC Syst Biol. · Pubmed #18986552 links to  free full text

Abstract: BACKGROUND: Systems biologic approaches such as Weighted Gene Co-expression Network Analysis (WGCNA) can effectively integrate gene expression and trait data to identify pathways and candidate biomarkers. Here we show that the additional inclusion of genetic marker data allows one to characterize network relationships as causal or reactive in a chronic fatigue syndrome (CFS) data set. RESULTS: We combine WGCNA with genetic marker data to identify a disease-related pathway and its causal drivers, an analysis which we refer to as "Integrated WGCNA" or IWGCNA. Specifically, we present the following IWGCNA approach: 1) construct a co-expression network, 2) identify trait-related modules within the network, 3) use a trait-related genetic marker to prioritize genes within the module, 4) apply an integrated gene screening strategy to identify candidate genes and 5) carry out causality testing to verify and/or prioritize results. By applying this strategy to a CFS data set consisting of microarray, SNP and clinical trait data, we identify a module of 299 highly correlated genes that is associated with CFS severity. Our integrated gene screening strategy results in 20 candidate genes. We show that our approach yields biologically interesting genes that function in the same pathway and are causal drivers for their parent module. We use a separate data set to replicate findings and use Ingenuity Pathways Analysis software to functionally annotate the candidate gene pathways. CONCLUSION: We show how WGCNA can be combined with genetic marker data to identify disease-related pathways and the causal drivers within them. The systems genetics approach described here can easily be used to generate testable genetic hypotheses in other complex disease studies.

Cameron B, Galbraith S, Zhang Y, Davenport T, Vollmer-Conna U, Wakefield D, Hickie I, Dunsmuir W, Whistler T, Vernon S, Reeves WC, Lloyd AR, Anonymous00251. · School of Medical Sciences, University of New South Wales, Sydney, Australia. · J Infect Dis. · Pubmed #17538884 No free full text.

Abstract: BACKGROUND: Infectious mononucleosis (IM) commonly triggers a protracted postinfective fatigue syndrome (PIFS) of unknown pathogenesis. METHODS: Seven subjects with PIFS with 6 or more months of disabling symptoms and 8 matched control subjects who had recovered promptly from documented IM were studied. The expression of 30,000 genes was examined in the peripheral blood by microarray analysis in 65 longitudinally collected samples. Gene expression patterns associated with PIFS were sought by correlation with symptom factor scores. RESULTS: Differential expression of 733 genes was identified when samples collected early during the illness and at the late (recovered) time point were compared. Of these genes, 234 were found to be significantly correlated with the reported severity of the fatigue symptom factor, and 180 were found to be correlated with the musculoskeletal pain symptom factor. Validation by analysis of the longitudinal expression pattern revealed 35 genes for which changes in expression were consistent with the illness course. These genes included several that are involved in signal transduction pathways, metal ion binding, and ion channel activity. CONCLUSIONS: Gene expression correlates of the cardinal symptoms of PIFS after IM have been identified. Further studies of these gene products may help to elucidate the pathogenesis of PIFS.

Craddock RC, Taylor R, Broderick G, Whistler T, Klimas N, Unger ER. · Centers for Disease Control and Prevention, Viral Exanthems and Herpesvirus Branch, Atlanta, GA 30333, USA. · Pharmacogenomics. · Pubmed #16610952 No free full text.

Abstract: The entropy correlation coefficient (ECC) is a useful tool for measuring statistical dependence between variables. We employed this tool to search for pairs of variables that correlated in the chronic fatigue syndrome (CFS) Computational Challenge dataset. Highly related variables are candidates for data reduction, and novel relationships could lead to hypotheses regarding the pathogenesis of CFS. METHODS: Data for 130 female participants in the Wichita (KS, USA) clinical study [1] was coded into numerical values. Metric data was grouped using Gaussian mixture models; the number of groups was chosen using Bayesian information content. The pair-wise correlation between all variables was computed using the ECC. Significance was estimated from 1000 iterations of a permutation test and a threshold of 0.01 was used to identify significantly correlated variables. RESULTS: The five dimensions of multidimensional fatigue inventory (MFI) were all highly correlated with each other. Seven Short Form (SF)-36 measures, four CFS case-defining symptoms and the Zung self-rating depression scale all correlated with all MFI dimensions. No physiological variables correlate with more than one MFI dimension. MFI, SF-36, CDC symptom inventory, the Zung self-rating depression scale and three Cambridge Neuropsychological Test Automated Battery (CANTAB) measures are highly correlated with CFS disease status. DISCUSSION: Correlations between the five dimensions of MFI are expected since they are measured from the same instrument. The relationship between MFI and Zung depression index has been previously reported. MFI, SF-36, and Centers for Disease Control and Prevention (CDC) symptom inventory are used to classify CFS; it is not surprising that they are correlated with disease status. Only one of the three CANTAB measures that correlate with disease status has been previously found, indicating the ECC identifies relationships not found with other statistical tools. CONCLUSION: The ECC is a useful tool for measuring statistical dependence between variables in clinical and laboratory datasets. The ECC needs to be further studied to gain a better understanding of its meaning for clinical data.

Broderick G, Craddock RC, Whistler T, Taylor R, Klimas N, Unger ER. · University of Alberta, Institute for Biomolecular Design, Edmonton, Alberta, T6G 2H7, Canada. · Pharmacogenomics. · Pubmed #16610951 No free full text.

Abstract: OBJECTIVES: To examine the potential of multivariate projection methods in identifying common patterns of change in clinical and gene expression data that capture the illness state of subjects with unexplained fatigue and nonfatigued control participants. METHODS: Data for 111 female subjects was examined. A total of 59 indicators, including multidimensional fatigue inventory (MFI), medical outcome Short Form 36 (SF-36), Centers for Disease Control and Prevention (CDC) symptom inventory and cognitive response described illness. Partial least squares (PLS) was used to construct two feature spaces: one describing the symptom space from gene expression in peripheral blood mononuclear cells (PBMC) and one based on 117 clinical variables. Multiplicative scatter correction followed by quantile normalization was applied for trend removal and range adjustment of microarray data. Microarray quality was assessed using mean Pearson correlation between samples. Benjamini-Hochberg multiple testing criteria served to identify significantly expressed probes. RESULTS: A single common trend in 59 symptom constructs isolates of nonfatigued subjects from the overall group. This segregation is supported by two co-regulation patterns representing 10% of the overall microarray variation. Of the 39 principal contributors, the 17 probes annotated related to basic cellular processes involved in cell signaling, ion transport and immune system function. The single most influential gene was sestrin 1 (SESN1), supporting recent evidence of oxidative stress involvement in chronic fatigue syndrome (CFS). Dominant variables in the clinical feature space described heart rate variability (HRV) during sleep. Potassium and free thyroxine (T4) also figure prominently. CONCLUSION: Combining multiple symptom, gene or clinical variables into composite features provides better discrimination of the illness state than even the most influential variable used alone. Although the exact mechanism is unclear, results suggest a common link between oxidative stress, immune system dysfunction and potassium imbalance in CFS patients leading to impaired sympatho-vagal balance strongly reflected in abnormal HRV.

Whistler T, Taylor R, Craddock RC, Broderick G, Klimas N, Unger ER. · Centers for Disease Control and Prevention, Viral Exanthems and Herpesvirus Branch, Atlanta, GA 30333, USA. · Pharmacogenomics. · Pubmed #16610950 No free full text.

Abstract: Quantitative trait analysis (QTA) can be used to test whether the expression of a particular gene significantly correlates with some ordinal variable. To limit the number of false discoveries in the gene list, a multivariate permutation test can also be performed. The purpose of this study is to identify peripheral blood gene expression correlates of fatigue using quantitative trait analysis on gene expression data from 20,000 genes and fatigue traits measured using the multidimensional fatigue inventory (MFI). A total of 839 genes were statistically associated with fatigue measures. These mapped to biological pathways such as oxidative phosphorylation, gluconeogenesis, lipid metabolism, and several signal transduction pathways. However, more than 50% are not functionally annotated or associated with identified pathways. There is some overlap with genes implicated in other studies using differential gene expression. However, QTA allows detection of alterations that may not reach statistical significance in class comparison analyses, but which could contribute to disease pathophysiology. This study supports the use of phenotypic measures of chronic fatigue syndrome (CFS) and QTA as important for additional studies of this complex illness. Gene expression correlates of other phenotypic measures in the CFS Computational Challenge (C3) data set could be useful. Future studies of CFS should include as many precise measures of disease phenotype as is practical.

Whistler T, Jones JF, Unger ER, Vernon SD. · Viral Exanthems and Herpesvirus Branch, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA. · BMC Physiol. · Pubmed #15790422 links to  free full text

Abstract: BACKGROUND: Chronic fatigue syndrome (CFS) is defined by debilitating fatigue that is exacerbated by physical or mental exertion. To search for markers of CFS-associated post-exertional fatigue, we measured peripheral blood gene expression profiles of women with CFS and matched controls before and after exercise challenge. RESULTS: Women with CFS and healthy, age-matched, sedentary controls were exercised on a stationary bicycle at 70% of their predicted maximum workload. Blood was obtained before and after the challenge, total RNA was extracted from mononuclear cells, and signal intensity of the labeled cDNA hybridized to a 3800-gene oligonucleotide microarray was measured. We identified differences in gene expression among and between subject groups before and after exercise challenge and evaluated differences in terms of Gene Ontology categories. Exercise-responsive genes differed between CFS patients and controls. These were in genes classified in chromatin and nucleosome assembly, cytoplasmic vesicles, membrane transport, and G protein-coupled receptor ontologies. Differences in ion transport and ion channel activity were evident at baseline and were exaggerated after exercise, as evidenced by greater numbers of differentially expressed genes in these molecular functions. CONCLUSION: These results highlight the potential use of an exercise challenge combined with microarray gene expression analysis in identifying gene ontologies associated with CFS.