Nada Lukkahatai, PhD, RN
Co-authors : B. Majors, BS; S. Reddy, PhD; B. Walitt, MD; L. Saligan, PhD
To examine differential expression of genes from fatigued fibromyalgia women with different levels of pain and catastrophizing.
Does the gene expression profile different in fatigued fibromyalgia women with high and low pain severities?
Does the gene expression profile different in fatigued fibromyalgia women with high and low catastrophizing?
Fibromyalgia (FM) is characterized by prolonged widespread muscle pain, profound fatigue, and sleep disturbance.
An estimated 10 million Americans and 200-400 million adults worldwide suffer from FM.
Prevalence: 3 times higher among women than men.
5.5 million FM patients visit the ambulatory care per year.
The etiology of the fibromyalgia is unknown.
1990 American College of
Rheumatology (ACR)
Criteria
1. History of chronic widespread pain
2. Pain in at least 11/18 tender point sites on digital palpation.
1 . Widespread Pain
Index (WPI) > 7 and
Symptom Severity
(SS) Scale score > 5 or
2. WPI = 3-6 and SS > 9
3. Symptoms present at similar level for > 3 months
4. No other disorder explaining the pain .
Catastrophizing is an exaggerated negative attention to symptoms.
Pain catastrophizing significantly predicts pain severity, chronic illness-related disability & emotional distress.
High fatigue catastrophizing is associated with high fatigue severity in breast cancer patients and predicts post cancer treatment fatigue.
Cognitive behavioral intervention focus on addressing maladaptive thinking improve fatigue severity and depression in chronic fatigue syndrome.
No study has explored the role of catastrophizing in influencing possible biologic correlates of pain and fatigue in
FM.
Little is known about the mechanisms that can explain both pain and fatigue symptoms experienced by FM patients.
Few studies have looked at possible genomic correlates of
FM.
Questionnaires:
Pain severity subscale of Brief Pain Inventory (BPI)
4-item subscale; the mean subscale score ranged from
0 to 10. The cutoff score that is clinically significant is 5.
General fatigue subscale - Multidimensional Fatigue
Inventory (MFI) 4-item subscale; score range from 4-20.
The score of ≥ 13 is significant fatigue.
Pain Catastrophizing Scale (PCS) = 13-item questionnaire; scores ranged from 0 - 52. Suggested clinical cut point is 16.
Biological sample collection and analysis:
Microarray technology using
Affymetrix GeneChip ® human genome U133 Plus 2.0 array was used on blood collected using Paxgene ® tubes.
Whole blood
Collected using
Paxgene tubes
Microarray
Differential gene expression was analyzed using Partek software.
Gene selection criteria: over 2fold increase or decrease, p <
0.05.
Network analyses by Ingenuity® software.
Extracted RNA
U133 Plus 2.0
9 Caucasian, female, diagnosed with FM, 26-64 years old
Min Max Mean (SD) Clinical
Cut-off
General Fatigue 13 20 17.1 (2.7) >13
Pain severity 0.5
6.3
4.1 (1.9)
Catastrophizing 4.0
36.0
17.0 (9.8)
>5
>16
High Pain Severity vs. Low Pain Severity (112 genes)
Up- regulated genes Down- regulated genes
Gene
Symbol
BATF2
Gene Title Function pvalue
Fold-
Change basic leucine zipper transcription factor, ATFlike 2
Protein binding/ protein dimerization activity
0.0002
2.1
CASP5 caspase 5, apoptosisrelated cysteine peptidase
Cyteine-type endopeptidase activity
0.0003
2.7
CCR1 chemokine
(C-C motif) receptor 1
CEACAM1 carcinoembry onic antigenrelated cell adhesion molecule 1
Molecular function, protein binding
0.001
2.4
COMMD6
COMM domain containing 6
C-C chemokine binding, chemokine (C-C motif) activity
0.0004
2.2
NF-kappa-B binding/ protein binding
0.001
4.1
Gene
Symbol
RPL7
SH2D1B
SIGLEC1 sialic acid binding Iglike lectin 1, sialoadhesin
UQCRB
ZCCHC2
Gene Title ribosomal protein L7
SH2 domain containing 1B ubiquinolcytochrome c reductase binding protein zinc finger,
CCHC domain containing 2
Function protein dimerization activity
Immune responses
Carbonydrate binding ubiquinolcytochrome-c reductase activity
Nucleic acid binding pvalue
Fold-
Change
0.02
0.04
0.04
0.05
0.05
-2.9
-2.8
-2.7
-2.6
-2.6
Network Analysis for high pain vs. low pain
High Catastrophizing vs. Low Catastrophizing (63 genes)
Up- regulated genes Down- regulated genes
Gene
Symbol
Gene Title function pvalue
Fold-
Change
SPP1 secreted phosphoprotein 1
Cytokine activity
0.01
2.1
TMTC1 transmembrane and
Integral to tetratricopeptide repeat containing membrane
1
0.02
2.5
SLC6A8 solute carrier family 6
(neurotransmitter transporter activity/ transporter,
Creatine molecular creatine), function member 8
0.03
2.4
NPRL3 nitrogen permease regulator-like 3
(S. cerevisiae)
Molecular function
0.04
2.1
HEMGN hemogen
Protein binding
0.04
2.2
Gene
Symbol
USP46
SEPT10
Gene Title ubiquitin specific peptidase 46 septin 10 function p-value
Fold-
Change
Ubiquitinspecific protease activity
GTP binding
0.001
0.002
-2.2
-2.0
CLEC4D
C-type lectin domain family 4, member D
Carbonydrate binding
0.005
-2.3
ZDHHC2 zinc finger,
DHHC-type containing 2
Zinc ion binding
0.005
-2.2
FAS
Fas (TNF receptor superfamily, member 6)
Identical protein binding/ signal transducer activity
0.010
-2.0
Network Analysis high vs. low catastrophizing
High vs Low Pain
Basic leucine zipper protein
(BATF2) is regulated by interferon and serves as a suppressor of activating protein-1
(AP-1).
Caspase 5, apoptosisrelated cysteine peptidase
(CASP5) and chemokine (C-C motif) receptor 1 (CCR1) are associated with immune response and inflammation in musculoskeletal disorders.
High vs Low Catastrophizing
Secreted phosphoprotein 1
(SPP1 or Osteopontin) is upregulated during inflammation and associated with muscular dystrophies.
Ubiquitin specific peptidase
46 (USP46) is a GABA regulation gene. In animal study, deletion of
USP46 is associated with depression-like behaviors in mice and rats.
STRESSOR
Inflammation
Inflammatory Cells: Leukocytes,
Lymphocytes, Platelets, Mast
Cells, Macrophages
CASP5
CCR1
BATF2
SPP1
Cytokines, Nerve Growth factor, prostaglandins,
Thromboxanes,
Leukotrienes, Serotonin
Hypothalamic-pituitary-adrenal (HPA) axis
Hypothalamus
Corticotropin releasing factors (CRF)
Anterior pituitary
Pituitary adrenocorticotropin
(ACTH)
Adrenal cortex
Physiological responses:
Pain and fatigue
USP46
Glucocorticoids
Fibromyalgia
Sympathetic nervous system
Adrenal gland:
Medulla
Acetylcholine (Ach) epinephrine norepinephrine
Bloodstream
Lymphoid organs
Behavioral responses:
Catastrophizing, depression
Leorey N. Saligan, PhD, CRNP
Tenure-Track Investigator
National Institute of Nursing Research, Intramural Research Program
Brian Walitt, MD, MPH, FACR
Associate Professor of Medicine
Georgetown University Medical Center
Benjamin Majors, BS
National Institute of Nursing Research, Intramural Research Program
Swarnalatha Reddy, PhD
National Institute of Nursing Research, Intramural Research Program
1. Wolfe, F., & Hawley, D. J. (1999). Evidence of disordered symptom appraisal in fibromyalgia: Increased rates of reported comorbidity and comorbidity severity . Clinical and Experimental Rheumatology , 17, 297
–303.
2. About Fibromyalgia: Prevalence, National Fibromyalgia Association , 2009, http://www.fmaware.org/
3. Lawrence, R.C. et al., (2008). Estimate of prevalence of arthritis and other rheumatic conditions in the United State. Part II. Arthritis
Rheumatology.
58(1), 26-35
4. Sacks JJ , Luo YH, Helmick CG. (2010) Prevalence of specific types of arthritis and other rheumatic conditions in the ambulatory health care system in the United States, 2001-2005 . Arth Care Res ; 62(4):460-4
5. Burger A., Dukes E., Martin, S. Edelsberg, J. Oster G. (2007). Characteristics and healthcare costs of patients with fibromyalgia syndrome . International Journal of Clinical Practice ; 61(9): 1498-1508
6. Light, KC, White, A.T., Tadler, S. Iacob, E., Light, AR. (2012). Genetics and gene expression involving stress and distress pathways in
Fibromyalgia with and without comorbid Chronic Fatigue Syndrome. Pain Research and Treatment ; doi:10.1155/2012/427869
7. Lee, YH, Choi SJ, Ji JD, Song GG. (2012), Candidate gene studies of fibromyalgia: a systematic review and meta-analysis.
Rheumatology International 32(2): 417-26
8. Wolfe F, Hauser W. (2010). Fibromyalgia diagnosis and diagnostic criteria . Ann. Med . 43(7): 495-502
9. Pavlin DJ, Sullivan MJ, Freund PR, Rosen K. (2005). Catastrophizing: a risk factor for postsurgical pain. Clinical journal of pain . 21(1):
83-90.
10. Edward RR, Cahalan C, Mensing G, Smith M, Haythornthwaite JA (2011). Pain, catastrophizing and depression in the rheumatic diseases . Nat Rev Rheumatol . 7(4): 216-24
11. Jacobsen PB, Andrykowski MA, Thors CL (2004). Relationship of catastrophizing to fatigue among women receiving treament for breast cancer. Journal of consult Clinical Psychology 72(2): 355-61
12. Andrykowski MA, Donovan KA, Loronga C. Jacobsen PB. (2010). Prevalence, predictors and characterisitics of off-treatment fatigue in breast cancer survivors. Cancer . 116(24). 5740-8. doi: 10.1002/cncr.25294. Epub 2010 Aug 23.
13. Friedberg F, Krupp LB (1994). A comparison of cognitive behavioral treatment for chronic fatigue syndrome and primary depression.
Clinical Infectious diseases . 18 Suppl 1: S105-10
14. Martinez-Lavin M. (2007). Biology and therapy of fibromyalgia. Stress, the stress response system, and fibromyalgia. Arthritis Res Ther .
9(4): 216
15. Castel LD, Abernethy AP, Li Y, Depuy V, Saville BR, Hartmann KE. (2007). Hazards for pain severity and pain interference with daily living, with exploratioin of brief pain inventory cutpoints, among women with metastatic breast cancer. Journal of Pain and symptoms
Management . 34(4): 380-92
16. Reeves WC, Wagner D, Nisenbaum, R, Jones JF, Gurbaxani B, Solomon L, Papanicolaou DA, Unger ER, Vernon SD, Heim, C. (2005).
Chronic fatigue syndrome- A clinically empirical approach to it definition and study. BMC Med . 15; 3-19
17. Riddle DL, Wade JB, Jiranek WA, Kong X. (2010). Preoperative pain catastrophizing predicts pain outcome after knee arthroplasty.
Clinical Orthopedic Related research . 468(3): 798-806
18. Ma H, Liang X, Chen Y, Pan K, Sun J, Wang H, Wang Q, Li Y, Zhao J, Li J, Chen M. Xia J. (2011) Decreased expression of BATF2 is associated with poor prognosis in hepatocellular carcinoma. International Journal of Cancer . 15; 128(4): 771-7
19 Huang W. Sowa G. (2011) Biomarker development for musculoskeletal diseases . PM.R
. 3 (6 Suppl 1): S39-44
20. Xu G, et al. (2005). Overexpression of osteopontin in rheumatoid synovial mononuclear cells is associated with joint inflammation, not with genetic polymorphism. Journal of Rheumatology . 32(3): 410-6
21. Fukuo Y. et al., (2011). Possible association between ubiquitin-specific peptidase 46 gene and major depressive disorder in the
Japanese population. J Affect Disord . 133 (1-2): 150-7
22 Zhang W. et al., (2011) Lysine 92 amino residue of USP46, a gene associated with “behavioral despair” in mice, influences the deubiquitinating enzyme activity. PLoS One. 2011;6(10):e26297. Epub 2011 Oct 17