gene expression – Page 2 – American ME and CFS Society

Convergent genomic studies identify association of GRIK2 and NPAS2 with chronic fatigue syndrome

Abstract:

BACKGROUND: There is no consistent evidence of specific gene(s) or molecular pathways that contribute to the pathogenesis, therapeutic intervention or diagnosis of chronic fatigue syndrome (CFS). While multiple studies support a role for genetic variation in CFS, genome-wide efforts to identify associated loci remain unexplored. We employed a novel convergent functional genomics approach that incorporates the findings from single-nucleotide polymorphism (SNP) and mRNA expression studies to identify associations between CFS and novel candidate genes for further investigation.

METHODS: We evaluated 116,204 SNPs in 40 CFS and 40 nonfatigued control subjects along with mRNA expression of 20,160 genes in a subset of these subjects (35 CFS subjects and 27 controls) derived from a population-based study.

RESULTS: Sixty-five SNPs were nominally associated with CFS (p<0.001), and 165 genes were differentially expressed (≥4-fold; p≤0.05) in peripheral blood mononuclear cells of CFS subjects. Two genes, glutamate receptor, ionotropic, kinase 2 (GRIK2) and neuronal PAS domain protein 2 (NPAS2), were identified by both SNP and gene expression analyses. Subjects with the G allele of rs2247215 (GRIK2) were more likely to have CFS (p=0.0005), and CFS subjects showed decreased GRIK2 expression (10-fold; p=0.015). Subjects with the T allele of rs356653 (NPAS2) were more likely to have CFS (p=0.0007), and NPAS2 expression was increased (10-fold; p=0.027) in those with CFS.

CONCLUSION: Using an integrated genomic strategy, this study suggests a possible role for genes involved in glutamatergic neurotransmission and circadian rhythm in CFS and supports further study of novel candidate genes in independent populations of CFS subjects.

Source: Smith AK, Fang H, Whistler T, Unger ER, Rajeevan MS. Convergent genomic studies identify association of GRIK2 and NPAS2 with chronic fatigue syndrome. Neuropsychobiology. 2011;64(4):183-94. doi: 10.1159/000326692. Epub 2011 Sep 9. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701888/ (Full article)

Gene expression alterations at baseline and following moderate exercise in patients with Chronic Fatigue Syndrome and Fibromyalgia Syndrome

Abstract:

OBJECTIVES: To determine mRNA expression differences in genes involved in signalling and modulating sensory fatigue, and muscle pain in patients with chronic fatigue syndrome (CFS) and fibromyalgia syndrome (FM) at baseline, and following moderate exercise.

DESIGN: Forty-eight patients with CFS only, or CFS with comorbid FM, 18 patients with FM that did not meet criteria for CFS, and 49 healthy controls underwent moderate exercise (25 min at 70% maximum age-predicted heart rate). Visual-analogue measures of fatigue and pain were taken before, during and after exercise. Blood samples were taken before and 0.5, 8, 24 and 48 h after exercise. Leucocytes were immediately isolated from blood, number coded for blind processing and analyses and flash frozen. Using real-time, quantitative PCR, the amount of mRNA for 13 genes (relative to control genes) involved in sensory, adrenergic and immune functions was compared between groups at baseline and following exercise. Changes in amounts of mRNA were correlated with behavioural measures and functional clinical assessments.

RESULTS: No gene expression changes occurred following exercise in controls. In 71% of patients with CFS, moderate exercise increased most sensory and adrenergic receptor’s and one cytokine gene’s transcription for 48 h. These postexercise increases correlated with behavioural measures of fatigue and pain. In contrast, for the other 29% of patients with CFS, adrenergic α-2A receptor’s transcription was decreased at all time-points after exercise; other genes were not altered. History of orthostatic intolerance was significantly more common in the α-2A decrease subgroup. FM-only patients showed no postexercise alterations in gene expression, but their pre-exercise baseline mRNA for two sensory ion channels and one cytokine were significantly higher than controls.

CONCLUSIONS: At least two subgroups of patients with CFS can be identified by gene expression changes following exercise. The larger subgroup showed increases in mRNA for sensory and adrenergic receptors and a cytokine. The smaller subgroup contained most of the patients with CFS with orthostatic intolerance, showed no postexercise increases in any gene and was defined by decreases in mRNA for α-2A. FM-only patients can be identified by baseline increases in three genes. Postexercise increases for four genes meet published criteria as an objective biomarker for CFS and could be useful in guiding treatment selection for different subgroups.

Source: Light AR, Bateman L, Jo D, Hughen RW, Vanhaitsma TA, White AT, Light KC. Gene expression alterations at baseline and following moderate exercise in patients with Chronic Fatigue Syndrome and Fibromyalgia Syndrome. J Intern Med. 2012 Jan;271(1):64-81. doi: 10.1111/j.1365-2796.2011.02405.x. Epub 2011 Jul 13. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3175315/ (Full article)

Moderate exercise increases expression for sensory, adrenergic, and immune genes in chronic fatigue syndrome patients but not in normal subjects

Abstract:

Chronic fatigue syndrome (CFS) is characterized by debilitating fatigue, often accompanied by widespread muscle pain that meets criteria for fibromyalgia syndrome (FMS). Symptoms become markedly worse after exercise. Previous studies implicated dysregulation of the sympathetic nervous system (SNS), and immune system (IS) in CFS and FMS.

We recently demonstrated that acid sensing ion channel (probably ASIC3), purinergic type 2X receptors (probably P2X4 and P2X5) and the transient receptor potential vanilloid type 1 (TRPV1) are molecular receptors in mouse sensory neurons detecting metabolites that cause acute muscle pain and possibly muscle fatigue. These molecular receptors are found on human leukocytes along with SNS and IS genes.

Real-time, quantitative PCR showed that 19 CFS patients had lower expression of beta-2 adrenergic receptors but otherwise did not differ from 16 control subjects before exercise. After a sustained moderate exercise test, CFS patients showed greater increases than control subjects in gene expression for metabolite detecting receptors ASIC3, P2X4, and P2X5, for SNS receptors alpha-2A, beta-1, beta-2, and COMT and IS genes for IL10 and TLR4 lasting from 0.5 to 48 hours (P < .05). These increases were also seen in the CFS subgroup with comorbid FMS and were highly correlated with symptoms of physical fatigue, mental fatigue, and pain.

These new findings suggest dysregulation of metabolite detecting receptors as well as SNS and IS in CFS and CFS-FMS.

PERSPECTIVE: Muscle fatigue and pain are major symptoms of CFS. After moderate exercise, CFS and CFS-FMS patients show enhanced gene expression for receptors detecting muscle metabolites and for SNS and IS, which correlate with these symptoms. These findings suggest possible new causes, points for intervention, and objective biomarkers for these disorders.

Source: Light AR, White AT, Hughen RW, Light KC. Moderate exercise increases expression for sensory, adrenergic, and immune genes in chronic fatigue syndrome patients but not in normal subjects. J Pain. 2009 Oct;10(10):1099-112. doi: 10.1016/j.jpain.2009.06.003. Epub 2009 Jul 31. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2757484/ (Full article)

A gene signature for post-infectious chronic fatigue syndrome

Abstract:

BACKGROUND: At present, there are no clinically reliable disease markers for chronic fatigue syndrome. DNA chip microarray technology provides a method for examining the differential expression of mRNA from a large number of genes. Our hypothesis was that a gene expression signature, generated by microarray assays, could help identify genes which are dysregulated in patients with post-infectious CFS and so help identify biomarkers for the condition.

METHODS: Human genome-wide Affymetrix GeneChip arrays (39,000 transcripts derived from 33,000 gene sequences) were used to compare the levels of gene expression in the peripheral blood mononuclear cells of male patients with post-infectious chronic fatigue (n = 8) and male healthy control subjects (n = 7).

RESULTS: Patients and healthy subjects differed significantly in the level of expression of 366 genes. Analysis of the differentially expressed genes indicated functional implications in immune modulation, oxidative stress and apoptosis. Prototype biomarkers were identified on the basis of differential levels of gene expression and possible biological significance.

CONCLUSION: Differential expression of key genes identified in this study offer an insight into the possible mechanism of chronic fatigue following infection. The representative biomarkers identified in this research appear promising as potential biomarkers for diagnosis and treatment.

Source: Gow JW, Hagan S, Herzyk P, Cannon C, Behan PO, Chaudhuri A. A gene signature for post-infectious chronic fatigue syndrome. BMC Med Genomics. 2009 Jun 25;2:38. doi: 10.1186/1755-8794-2-38. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2716361/ (Full article)

An integrated approach to infer causal associations among gene expression, genotype variation, and disease

Abstract:

Gene expression data and genotype variation data are now capable of providing genome-wide patterns across many different clinical conditions. However, the separate analysis of these data has limitations in elucidating the complex network of gene interactions underlying complex traits, such as common human diseases. More information about the identity of key driver genes of common diseases comes from integrating these two heterogeneous types of data. We developed a two-step procedure to characterize complex diseases by integrating genotype variation data and gene expression data.

The first step elucidates the causal relationship among genetic variation, gene expression level, and disease. Based on the causal relationship determined at the first step, the second step identifies significant gene expression traits whose effects on disease status or whose responses to disease status are modified by the specific genotype variation. For the selected significant genes, a pathway enrichment analysis can be performed to identify the genetic mechanism of a complex disease. The proposed two-step procedure was shown to be an effective method for integrating three different levels of data, i.e., genotype variation, gene expression and disease status.

By applying the proposed procedure to a chronic fatigue syndrome (CFS) dataset, we identified a list of potential causal genes for CFS, and found an evidence for difference in genetic mechanisms of the etiology between CFS without ‘a major depressive disorder with melancholic features’ (CFS) and CFS with ‘a major depressive disorder with melancholic features’ (CFS-MDD/m). Especially, the SNPs within NR3C1 gene were shown to differently influence the susceptibility of developing CFS and CFS-MDD/m through integrative action with gene expression levels.

Source: Lee E, Cho S, Kim K, Park T. An integrated approach to infer causal associations among gene expression, genotype variation, and disease. Genomics. 2009 Oct;94(4):269-77. doi: 10.1016/j.ygeno.2009.06.002. Epub 2009 Jun 18. http://www.sciencedirect.com/science/article/pii/S0888754309001347 (Full article)

Exploration of the gene expression correlates of chronic unexplained fatigue using factor analysis

Abstract:

OBJECTIVE: To identify biomarkers of chronic fatigue syndrome (CFS) and related disorders through analysis of microarray data, pathology test results and self-report symptom profiles.

METHOD: To empirically derive the symptom domains of the illnesses, factor analysis was performed on responses to self-report questionnaires (multidimensional fatigue inventory, Centers for Disease Control and Prevention (CDC) symptom inventory and Zung depression scale) before validation with independent datasets. Gene expression patterns that distinguished subjects across each factor dimension were then sought.

RESULTS: A four-factor solution was favored, featuring ‘fatigue’ and ‘mood disturbance’ factors. Scores on these factors correlated with measures of disability on the Short Form (SF)-36. A total of 57 genes that distinguished subjects along each factor dimension were identified, although the separation was significant only for subjects beyond the extreme (15th and 85th) percentiles of severity. Clustering of laboratory parameters with expression of these genes revealed associations with serum measurements of pH, electrolytes, glucose, urea, creatinine, and liver enzymes (aspartate amino transferase [AST] and alanine amino transferase [AST]); as well as hematocrit and white cell count.

CONCLUSION: CFS is a complex syndrome that cannot simply be associated with changes in individual laboratory tests or expression levels of individual genes. No clear association with gene expression and individual symptom domains was found. However, analysis of such multifacetted datasets is likely to be an important means to elucidate the pathogenesis of CFS.

Source: Fostel J, Boneva R, Lloyd A. Exploration of the gene expression correlates of chronic unexplained fatigue using factor analysis. Pharmacogenomics. 2006 Apr;7(3):441-54. https://www.ncbi.nlm.nih.gov/pubmed/16610954

Gene expression profile exploration of a large dataset on chronic fatigue syndrome

Abstract:

OBJECTIVE: To gain understanding of the molecular basis of chronic fatigue syndrome (CFS) through gene expression analysis using a large microarray data set in conjunction with clinically administrated questionnaires.

METHOD: Data from the Wichita (KS, USA) CFS Surveillance Study was used, comprising 167 participants with two self-report questionnaires (multidimensional fatigue inventory [MFI] and Zung depression scale [Zung]), microarray data, empiric classification, and others. Microarray data was analyzed using bioinformatics tools from ArrayTrack.

RESULTS: Correspondence analysis was applied to the MFI questionnaire to select the 23 samples having either the most or the least fatigue, and to the Zung questionnaire to select the 26 samples having either the most or least depression; ten samples were common, resulting in a total of 39 samples. The MFI and Zung-based CFS/non-CFS (NF) classifications on the 39 samples were consistent with the empiric classification. Two differentially-expressed gene lists were determined, 188 fatigue-related genes and 164 depression-related genes, which shared 24 common genes and involved 11 common pathways. Principal component analysis based on 24 genes clearly separates 39 samples with respect to their likelihood to be CFS. Most of the 24 genes are not previously reported for CFS, yet their functions are consistent with the prevailing model of CFS, such as immune response, apoptosis, ion channel activity, signal transduction, cell-cell signaling, regulation of cell growth and neuronal activity. Hierarchical cluster analysis was performed based on 24 genes to classify 128 (=167-39) unassigned samples. Several of the 11 identified common pathways are supported by earlier findings for CFS, such as cytokine-cytokine receptor interaction and neuroactive ligand-receptor interaction. Importantly, most of the 11 common pathways are interrelated, suggesting complex biological mechanisms associated with CFS.

CONCLUSION: Bioinformatics is critical in this study to select definitive sample groups, analyze gene expression data and gain insight into biological mechanisms. The 24 identified common genes and 11 common pathways could be important in future studies of CFS at the molecular level.

Source: Fang H, Xie Q, Boneva R, Fostel J, Perkins R, Tong W. Gene expression profile exploration of a large dataset on chronic fatigue syndrome. Pharmacogenomics. 2006 Apr;7(3):429-40. https://www.ncbi.nlm.nih.gov/pubmed/16610953

Gene expression profiling in the chronic fatigue syndrome

Fatigue is a symptom found in many conditions of disease and illness. Although, unfrequently recognized by the medical profession, it is often of major importance for the patients. Chronic fatigue was reported by 5.9% of the Swedish population in a large telephone-based interview with 31 406 individuals in the Swedish twin registry (STR) [1]. The fatigue had lasted for more than 6 months and caused impairment, e.g. >25% reduction of working capacity. When at least four of eight criteria included in the current definition of chronic fatigue syndrome (CFS) [2] was added 2.4% reported that they suffered from a CFS-like illness.

This costly condition is still an intriguing issue for researchers and clinicians, and ambiguities in the definition have recently been focused upon [3, 4]. An empirical test of the definition was performed with data from the STR where five subgroups were identified: ‘CFS-like’, ‘residual’, ‘rheumatic’, ‘depressive’ and ‘acute physical syndrome’ [5].

We wanted to identify genes in peripheral blood mononuclear cells (PBMCs), which may play an important role in the pathogenesis and diagnostics of CFS, using microarray technology. PBMCs can serve as indicators of illness processes occurring in different parts of the human body. Patients with CFS from a clinic of infectious diseases at a university hospital were stratified according to the STR study findings [5] to sex, illness classification (ICD-10), illness onset type, illness duration and number of symptoms (Table 1).

You can read the rest of this article here: http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2796.2005.01548.x/full

Source: Gräns H, Nilsson P, Evengard B. Gene expression profiling in the chronic fatigue syndrome. J Intern Med. 2005 Oct;258(4):388-90. http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2796.2005.01548.x/full (Full article)