Rheumatology International

, Volume 35, Issue 4, pp 635–642 | Cite as

Profile of circulating microRNAs in fibromyalgia and their relation to symptom severity: an exploratory study

  • Jan L. Bjersing
  • Maria I. Bokarewa
  • Kaisa Mannerkorpi
Original Article - Genes and Disease

Abstract

Fibromyalgia (FM) is characterized by generalized chronic pain and reduced pain thresholds. Disturbed neuroendocrine function and impairment of growth hormone/insulin-like growth factor-1 is common. However, the pathophysiology of FM is not clear. MicroRNAs are important regulatory factors reflecting interface of genes and environment. Our aim was to identify characteristic microRNAs in FM and relations of specific microRNAs with characteristic symptoms. A total of 374 circulating microRNAs were measured in women with FM (n = 20; median 52.5 years) and healthy women (n = 20; 52.5 years) by quantitative PCR. Pain thresholds were examined by algometry. Pain [fibromyalgia impact questionnaire (FIQ) pain] levels were rated (0–100 mm) using FIQ. Fatigue (FIQ fatigue) was rated (0–100 mm) using FIQ and multidimensional fatigue inventory general fatigue. Sleep quantity and quality (1–4) rated from satisfactory to nonsatisfactory. Higher scores indicate more severe symptoms. Eight microRNAs differed significantly between FM and healthy women. Seven microRNAs, miR-103a-3p, miR-107, let-7a-5p, miR-30b-5p, miR-151a-5p, miR-142-3p and miR-374b-5p, were lower in FM. However, levels of miR-320a were higher in FM. MiR-103a-3p correlated with pain (r = 0.530, p = 0.016) and sleep quantity (r = 0.593, p = 0.006) in FM. MiR-320a correlated inversely with pain (r = −0.468, p = 0.037). MiR-374b-5p correlated inversely with pain threshold (r = −0.612, p = 0.004). MiR-30b-5p correlated with sleep quantity (r = 0.509, p = 0.022), and let-7a-5p was associated with sleep symptoms. When adjusted for body mass index, the correlation of sleep quantity with miR-103a and miR-30b was no longer significant. To our knowledge, this is the first study of circulating microRNAs in FM. Levels of several microRNAs differed significantly in FM compared to healthy women. Three microRNAs were associated with pain or pain threshold in FM.

Keywords

Fibromyalgia Pain MicroRNAs Biomarkers Human 

Abbreviations

FM

Fibromyalgia syndrome

FIQ

Fibromyalgia impact questionnaire

MFI-20

Multidimensional fatigue inventory

MFIGF

Multidimensional fatigue inventory, subscale of general fatigue

BMI

Body mass index

Introduction

Fibromyalgia (FM) is a disorder characterized by chronic pain and reduced pain threshold. The pathophysiology of FM involves disturbed neuroendocrine function, including impaired function of the growth hormone/insulin-like growth factor-1 axis [1, 2, 3]. Obesity is common in FM, and there is a possible involvement of adipokines [4]. FM is associated with sleep disturbance interacting with pain and fatigue [5, 6]. FM is regarded as a multifactorial disorder, possibly involving both environmental and genetic factors.

Familial aggregation of FM is common [7, 8], and genetic studies have generated a number of candidate FM genes. However, these studies have generally been limited and generating somewhat divergent results [9]. A large-scale candidate study was recently performed that identified three novel candidate genes [10] associated with the disorder.

Being a chronic condition, it is likely that the ongoing disease process and environmental influences cause long-term changes in gene expression in FM. Evidence is emerging for the involvement of such epigenetic processes in chronic pain [11] but has not been studied specifically in FM patients.

MicroRNAs have in recent years been identified as important modulators of gene expression in disease processes and physiological pathways. The microRNAs are highly evolutionary conserved, short noncoding RNA molecules, approximately 20–22 nucleotides in length. They inhibit gene expression posttranscriptionally either by inhibition of translation or by degradation of the target messenger RNA. MicroRNAs may regulate at least 30 % of human genes [12], and individual microRNAs can repress hundreds of genes [13]. Conversely, one messenger RNA can be the target of several microRNAs. This enables the coordination of complex programs of gene expression [14] and precise adjustments of protein output [13]. Changes in a single or small group of microRNAs may therefore sensitively reflect changes involving a large number of different messenger RNAs.

From a clinical viewpoint, it is interesting that microRNAs usually have a high stability in biological samples such as serum and plasma. Extracellular microRNAs in the bloodstream are denoted circulating microRNAs [14]. MicroRNAs can be released into biological fluids in extracellular microvesicles called exosomes or packaged in complexes associated with the argonaute proteins. The high stability of extracellular microRNAs and different types of compartmentalization outside cells indicates the possibility of extracellular functions for microRNAs [15].

Therefore, circulating microRNA has received increasing attention as useful biomarkers and important regulatory factors in a number of diseases. The pharmacological use of microRNAs has received considerable interest for several reasons, including wide-ranging regulatory functions and high stability [16, 17].

Different expression of microRNAs in patients with FM compared to healthy controls was recently reported [18]. However, the expression of circulating microRNAs has not been previously studied in FM.

The aim of this study was to identify circulating microRNAs with expression specific for FM and to determine their relation to typical FM symptoms.

Materials and methods

Study design and subjects

Twenty patients with FM were compared to twenty age-matched healthy controls. Serum was collected at rest (n = 40). Serum samples were acquired by venipuncture of the cubital vein. The blood samples were centrifuged at 800g for 3 min, aliquoted and stored frozen at −70 °C until use.

FM patients and healthy controls were recruited by advertisements in local newspapers. Criteria for inclusion of FM patients: women with FM, aged 20–60 years, and with interest in exercising outdoors twice a week for 15 weeks, willing to participate at blood tests [19]. FM was defined by the ACR 1990 criteria [20]: a history of long-lasting generalized pain and with pain in at least 11 of 18 tender points examined by manual palpation. Criteria for exclusion of FM patients: patients not speaking or reading Swedish, other severe somatic or psychiatric disease.

Criteria for inclusion of healthy controls: age-matched healthy women. Criteria for exclusion: patients not speaking or reading Swedish, symptoms indicating FM, other severe somatic or psychiatric disease.

The median age of FM patients was 52.5 (28.3–56.0) years and of healthy controls 52.5 (48.3–55.8, n.s.) years. The symptom duration of FM patients was 9 (7–13) years. The median body mass index (BMI) was higher in FM patients. 28.1 kg/m2 (26.7–29.8) compared to healthy controls 23.1 (20.9–24.7 p < 0.0002). BMI in the healthy controls was typical for Swedish women in this age group [21]. For symptoms and pharmacologic treatment of patients and healthy controls, see Table 1.
Table 1

Symptoms and pharmacologic treatment of FM patients and healthy controls

 

FM patients (n = 20)

Healthy controls (n = 20)

Group comparison (p valuea)

Tender points, n

15.5 (13.3–16.8)

 

Pain threshold

220.4 (167–271.8)

322.4 (289.5–385.8)

p < 0.0001

FIQ pain

72.5 (53–85.3)

0 (0–0)

p < 0.0001

FIQ fatigue

80 (71–91.5)

2 (0–17.5)

p < 0.0001

MFIGF

15.5 (14–19.8)

6.5 (4–9)

p < 0.0001

BMI (kg/m2)

28.1 (26.7–29.8)

23.1 (20.9–24.7)

p < 0.0002

Monoamine reuptake inhibitorsb

7 (35 %)

1 (5 %)

 

Sedatives

8 (40 %)

0

 

NSAIDs

15 (75 %)

0

 

Opioids for mild to moderate pain

5 (25 %)

0

 

Triptans

0

1 (5 %)

 

Median values and interquartile range are indicated

aMann–Whitney U test

bMonoamine reuptake inhibitors used were tryptizol (2/20 FM) or SNRI (4/20 FM) or SSRI (1/20 FM, 1/20 healthy control)

Use of monoamine reuptake inhibitors, i.e. selective serotonin reuptake inhibitors (SSRI) and combined serotonin and noradrenalin reuptake inhibitors (SNRI) and tricyclic nonselective reuptake inhibitors, was common among FM patients. Doses were low and used as a treatment of chronic pain, see Table 1.

Sleep disturbance is common in FM, and forty per cent of women with FM (8/20) used sedatives: benzodiazepines (7/20) and/or hydroxyzine (2/20) a sedative anti-histamine compound. Seventy-five per cent of FM patients used analgetics, mainly nonsteroidal anti-inflammatory drugs (NSAIDs, 15/20), sometimes in combination with opioids for mild to moderate pain (tramadol, codeine; 5/20). One healthy control occasionally medicated with triptans for migraine but she did not have migraine at examination and sampling of blood. Another healthy control used low-dose SSRI but she did not suffer from depression or FM. Medication was not an exclusion criterium in this study, see Table 1.

Clinical measurements

The pain threshold was examined by using an algometer (Somedic Production AB, Sollentuna, Sweden), measured in kilopascal [22]. The pain threshold was measured in two tender point locations in the upper and lower extremities, respectively. The mean value was applied, and a higher value indicates better health. Levels of pain [fibromyalgia impact questionnaire (FIQ) pain] were rated on a 0–100-mm scale FIQ [23]. Fatigue was rated on a visual analogue scale (0–100) of the FIQ [23] which gives an estimation of global fatigue, as well as with the multidimensional fatigue inventory (MFI-20) [24] subscale of general fatigue (MFIGF, range 4–20), which estimates general fatigue by questions related to feeling “fit”, “tired” and “rested”. Both instruments reflect fatigue during the last week, and a higher score indicates more severe fatigue.

Sleep quantity and quality (1–4) were rated by patients from satisfactory to nonsatisfactory, using two questions about the patients' quantity and quality of sleep [25]: “Do you think you get enough sleep?” and “On the whole, how do you think you sleep?” A higher score indicates worse sleep with regard to quantity and quality, respectively [25].

Sample preparation of circulating microRNA

Isolation of total RNA from serum was conducted at Exiqon Services, Denmark. Total RNA was extracted from serum using the Qiagen miRNeasy Mini Kit (Qiagen, Hilden. Germany. Cat no 217004). Serum was thawed on ice and centrifuged at 3,000×g for 5 min in a 4 °C microcentrifuge. An aliquot of 200 μL of serum per sample was transferred to a new microcentrifuge tube, and 750 μL of a Qiazol mixture containing 1.25 μg/mL of MS2 bacteriophage RNA (Roche. Cat. no. 10165948001) was added to the serum. The tube was mixed and incubated for 5 min followed by the addition of 200 μL chloroform. The tube was mixed, incubated for 2 min and centrifuged at 12,000×g for 15 min in a 4 °C microcentrifuge. The upper aqueous phase was transferred to a new microcentrifuge tube, and 1.5 volume of 100 % ethanol was added. The contents were mixed thoroughly, and 750 μL of the sample was transferred to a Qiagen RNeasy Mini spin column in a collection tube followed by centrifugation at 15,000×g for 30 s at room temperature. The process was repeated until all remaining sample had been loaded. The Qiagen RNeasy Mini spin column was rinsed with 700-μL Qiagen RWT buffer and centrifuged at 15,000×g for 1 min at room temperature followed by another rinse with 500-μL Qiagen RPE buffer and centrifuged at 15,000×g for 1 min at room temperature. A rinse step (500 μL Qiagen RPE buffer) was repeated twice. The Qiagen RNeasy Mini spin column was transferred to a new collection tube and centrifuged at 15,000×g for 2 min at room temperature. The Qiagen RNeasy Mini spin column was transferred to a new microcentrifuge tube, and the lid was left uncapped for 1 min to allow the column to dry. Total RNA was eluted by adding 50 μL of RNase-free water to the membrane of the Qiagen RNeasy mini spin column and incubating for 1 min before centrifugation at 15,000×g for 1 min at room temperature. The RNA was stored in a −80 °C freezer.

MicroRNA real-time qPCR

Amplification and detection of microRNAs were conducted at Exiqon Services, Denmark. RNA was reverse-transcribed in 40-μL reactions using the miRCURY LNA Universal RT microRNA PCR, Polyadenylation and cDNA synthesis kit (Exiqon, Vedbaek. Denmark. Cat no 203301). An artificial RNA (RNA Spike-in. Exiqon, Vedbaek. Denmark. Cat no. 20323) was added to the reverse transcription step. This control was used to confirm that the reverse transcription and amplification occured with equal efficiency in all samples. cDNA was diluted and assayed in PCRs according to the protocol for miRCURY LNA Universal RT microRNA PCR; each microRNA was assayed by qPCR on the microRNA Ready-to-Use PCR microchip (Exiqon, Denmark, Cat No 203608) for detection of human microRNAs. Negative controls excluding template from the reverse transcription reaction were included and profiled like the samples. The amplification was performed in a LightCycler 480 Real-Time PCR System (Roche) in 384-well plates. The amplification curves were analysed using the Roche LC software, both for determination of crossing point (Cp) value, by the second derivative method, and for melting curve analysis.

Data analysis

A total of 374 human microRNAs and five snRNA reference genes were assayed in each sample. A detection threshold of Cp < 38 was defined to achieve optimal information quality [26]. To be included in the data analysis, microRNA assays were required either to have a negative control with Cp ≥ 40 (313 microRNAs) or a difference of 5 Cps or more to the negative control, in at least 50 % of the individuals (7 microRNAs).

Normalization

Normalization of the microRNA RT-qPCR data was performed with rank normalization by calculation of fractional rank [27]. First, the Cp value of each individual microRNA species was ranked within each sample. The highest amount of microRNA in one sample was assigned the highest rank in that sample. In the next step, the rank of one microRNA was divided by the total number of detectable microRNAs in the sample. This was the fractional rank.

Thus, for each microRNA species in a sample, the fractional rank was calculated using the following formula: Fractional rank = individual rank (rank of one individual microRNA species in the sample)/(number of all expressed microRNAs in sample +1).

Statistical analysis

Fractional ranks of FM patients and healthy controls were compared using with Mann–Whitney U test. Group comparisons between healthy controls and FM patients were performed for microRNAS expressed with a Cp < 38 in 50 % or more of subjects. Thus, 150 microRNAs were compared.

Mann–Whitney U test was used for comparisons between groups. In the identification of the differences in microRNA expression between the groups, Bonferroni–Holm step down procedure was used to define the critical p values for multiple comparisons of 150 microRNAs. The critical p value after eight steps was 3.5 x 10−4, and eight microRNAs were identified that were significantly different between FM patients and healthy controls (Table 2).
Table 2

List of microRNAs with significantly different expression between FM patients and healthy controls

 

Healthy controls

FM patients

Comparison of groups p valuea

miR-103a-3p

0.888 (0.878–0.907)

0.856 (0.838–0.868)

5.0 × 10−7

miR-107

0.817 (0.812–0.821)

0.781 (0.770–0.802)

3.4 × 10−6

miR-320a

0.884 (0.871–0.893)

0.912 (0.897–0.928)

3.4 × 10−6

let-7a-5p

0.559 (0.528–0.589)

0.504 (0.483–0.518)

1.8 × 10−5

miR-30b-5p

0.790 (0.774–0.802)

0.761 (0.742–0.774)

1.8 × 10−5

miR-151a-5p

0.724 (0.713–0.746)

0.689 (0.661–0.706)

2.1 × 10−5

miR-142-3p

0.926 (0.914–0.949)

0.899 (0.890–0.912)

3.9 × 10−5

miR-374b-5p

0.567 (0.528–0.593)

0.473 (0.447–0.541)

6.9 × 10−5

MicroRNAs are listed in order of significance, starting with the lowest p values. Normalized levels of microRNAs are expressed as the fractional rank. Higher fractional rank represents higher levels of microRNA. Median fractional rank values and interquartile range are indicated

aMann–Whitney U test

In the next step, relations between these identified microRNAs and typical FM symptoms were examined with the Spearman correlation coefficient. Partial correlational analyses were performed to adjust for BMI. To control for possible Type I errors, the upper limit of number of false significances was calculated (using the alpha level 0.05) by the following formula: (number of tests − number of significant tests on level of alpha) × alpha/(1 − alpha). Statistical analysis was performed using SPSS (version 19.0.0 for Mac).

Ethics

The study was approved by the ethics committee of Sahlgrenska University Hospital. Written and verbal information was given to all patients, and written consent was obtained from all patients according to the requirements in the Helsinki Declaration.

Results

Differences in microRNA expression between FM patients and healthy controls

MicroRNA expression was compared between FM patients and healthy controls. Eight microRNAs were significantly different in expression after applying Bonferroni correction for multiple testing (Table 2).

One microRNA, miR-320a, was higher in FM than healthy controls. Seven microRNAs, miR-103a-3p, miR-107, let-7a-5p, miR-30b-5p, miR-151a-5p, miR-142-3p and miR-374b-5p, were lower in FM compared to healthy controls.

Disease-associated microRNAs and symptom severity

After identifying eight microRNAS differentially expressed in the FM patient sample, we wanted to determine if these microRNAs were involved in typical FM related symptoms. Thus, the relation of the eight identified microRNAs to pain, fatigue and sleep was tested in the FM patients.

MiR-103a-3p correlated with pain (r = 0.530, p = 0.016, n = 20) and with sleep quantity (r = 0.593, p = 0.006, n = 20) in the FM patients. MiR-320a was inversely correlated with pain (r = −0.468, p = 0.037, n = 20). See Table 3.
Table 3

Correlation of disease-associated microRNAs with common symptoms in FM

 

FIQ pain

Pain threshold

FIQ fatigue

MFIGF

Sleep quantity

Sleep quality

miR-103a-3p

rs = 0.530

rs = 0.016

rs = 0.313

rs = 0.194

rs = 0.593

rs = 0.381

p = 0.016

p = 0.947

p = 0.179

p = 0.412

p = 0.006

p = 0.098

n = 20

n = 20

n = 20

n = 20

n = 20

n = 20

miR-107

rs = 0.359

rs = −0.35

rs = 0.154

rs = 0.197

rs = 0.208

rs = 0.054

p = 0.120

p = 0.131

p = 0.518

p = 0.406

p = 0.378

p = 0.821

n = 20

n = 20

n = 20

n = 20

n = 20

n = 20

miR-320a

rs = −0.468

rs = 0.093

rs = 0.023

rs = 0.172

rs = −0.114

rs = −0.088

p = 0.037

p = 0.698

p = 0.925

p = 0.468

p = 0.631

p = 0.712

n = 20

n = 20

n = 20

n = 20

n = 20

n = 20

let-7a-5p

rs = 0.435

rs = −0.425

rs = 0.107

rs = 0.206

rs = 0.44

rs = 0.267

p = 0.055

p = 0.062

p = 0.653

p = 0.384

p = 0.052

p = 0.255

n = 20

n = 20

n = 20

n = 20

n` = 20

n = 20

miR-30b-5p

rs = 0.403

rs = −0.384

rs = 0.02

rs = 0.133

rs = 0.509

rs = 0.352

p = 0.078

p = 0.094

p = 0.932

p = 0.577

p = 0.022

p = 0.128

n = 20

n = 20

n = 20

n = 20

n = 20

n = 20

miR-151a-5p

rs = 0.102

rs = −0.138

rs = 0.079

rs = 0.089

rs = 0.214

rs = 0.306

p = 0.668

p = 0.563

p = 0.741

p = 0.711

p = 0.364

p = 0.189

n = 20

n = 20

n = 20

n = 20

n = 20

n = 20

miR-142-3p

rs = -0.162

rs = 0.000

rs = −0.089

rs = 0.017

rs = −0.135

rs = 0.145

p = 0.494

p = 1.000

p = 0.709

p = 0.944

p = 0.571

p = 0.542

n = 20

n = 20

n = 20

n = 20

n = 20

n = 20

miR-374b-5p

rs = 0.193

rs = -0.612

rs = −0.365

rs = −0.227

rs = 0.114

rs = −0.081

p = 0.414

p = 0.004

p = 0.114

p = 0.335

p = 0.632

p = 0.734

n = 20

n = 20

n = 20

n = 20

n = 20

n = 20

Spearman’s rho (rs), two-tailed significance (p) and number of subjects (n) are presented

p values <0.05 are indicated in bold

MiR-374b-5p was inversely correlated with pain threshold (rho = −0.612, p = 0.004, n = 20).

MiR-30b-5p correlated with sleep quantity in the FM patients (rho = 0.509, p = 0.022, n = 20), and let-7a-5p tended to associate with sleep quantity and pain. See Table 3.

Analyses of correlations between disease-associated microRNAs with symptoms of pain, fatigue and sleep comprised a total of 48 comparisons, and the upper level of number of false significances was 2.26, which means that two of the significances might be false.

After correction for BMI, correlation of pain for miR-103a (r = 0.556; p = 0.013) and miR-320a (r = −0.459; p = 0.047) and correlation of pain threshold with miR-374b (r = −0.543; p = 0.016) remained significant. On the other hand, correlation of sleep quantity with miR-103a and miR-30b was no longer significant after correction for BMI.

Discussion

The pathophysiology of chronic pain and fatigue in FM is unclear. MicroRNAs are increasingly recognized as important regulatory factors with diagnostic and therapeutic potential in many human diseases. Identifying alterations in microRNA networks in FM may give new leads to the pathogenesis of this condition.

FM is a disorder characterized by chronic pain and reduced pain threshold, also associated with sleep disturbances, fatigue and distress. In this study, we identified several microRNAs that were differently expressed in the FM patients and the healthy controls. The FM-specific expression pattern of these circulating microRNAs is interesting both for the potential use as biomarkers and as clues to the difference between FM patients and healthy controls. These differences may reflect pathological changes in patients with FM. Changes in relative microRNA levels may also represent adaptive responses to minimize the effects of the disease and its symptoms.

The levels of the identified microRNAs were related to the severity of symptoms common in FM.

MiR-320a was higher in FM and was inversely correlated with pain in these patients. MiR-320 promotes neurite growth, indicating a potential beneficial role in neuronal regeneration [28]. MiR-320a was previously shown to be upregulated in bladder biopsies from patients with chronic bladder pain syndrome, and it was indicated that this microRNA could downregulate the substance P/neurokinin receptor 1 [29]. In complex regional pain syndrome, let-7a and miR-320 were differentially expressed [30].

Let-7 microRNAs are highly evolutionary conserved; important roles in mammals include proliferation, early differentiation and brain development; and they are prognostic markers in several malignancies [31, 32]. Let-7 microRNAs are upregulated by morphine and repress µ-opioid receptor expression. This indicates involvement of let-7 microRNAs in regulation of the endogenous opioid system and in opioid tolerance [33].

MiR-142 was reduced in FM patients. This microRNA has been associated with expression of dopamine D1 receptor [34] and with genes involved in diurnal rhythms [35]. Dopaminergic neurons are involved in diurnal rhythms [36]. The dopamine D1 receptor mRNA also contains target sequences of both miR-103 and miR-107.

Interestingly, one of three recently identified FM candidate genes TAAR1 [10] can modulate dopaminergic neurotransmission [37] and gene polymorphisms in the dopamine transporter [38], dopamine D3-receptors [39] and dopamine D4-receptors [40] may be linked to FM. Alterations in dopaminergic neurotransmission could be linked to sleep disturbance in FM patients.

MiR-103 and miR-107 belong to the so-called miR-15/107 group of microRNA genes and are closely related sharing a large proportion of mRNA targets [41]. Apart from regulation of metabolism, they appear to serve key functions in cell division, stress responses [41, 42] and exercise [41]. Several of the microRNAs identified in this study, especially miR-103 and miR-107, have previously been associated with regulation of energy metabolism and metabolic disease [43, 44]. However, miR-107 is also downregulated by TLR-4 and has been suggested to be a link between obesity and inflammation [45]. MiR-374 has also been linked to metabolic regulatory functions relating to lipid metabolism and adipocyte differentiation [46].

Reduced levels of miR-103 and miR-107 were seen both in elderly subjects [47] and in early Alzheimers disease [48, 49]. Expression of miR-103 was reduced in lymphocytes from patients with chronic fatigue syndrome [50].

The serum microRNAs in FM identified in this study showed a different pattern from cerebrospinal fluid microRNAs in FM [18]. This could indicate that different processes are dominant in the periphery compared to the central nervous system.

The reduced levels of these microRNAs in FM may reflect impaired anabolic processes causing increased vulnerability to stress. The fact that miR-103 and several other microRNAs are positively linked with symptom severity in FM patients may represent an adaptive but insufficient response to stress.

The identification of differentially expressed circulating microRNAs might be useful in the development of biomarkers assessing disease activity.

This is an exploratory study on microRNA levels in women with FM. After identifying circulating microRNAs linked to the sample of FM patients, we investigated their relation to symptoms in FM. For this reason, the study includes many analyses. Due to multiple analyses, the significance level should be interpreted with caution, and the upper limit of expected number of false significances is presented in the results section. After correction for BMI, the correlation of miR-103a and miR-30b to sleep was no longer significant.

Conclusion

This study shows for the first time a disease-specific pattern of circulating microRNAs in FM patients. Several of the identified microRNAs were associated with pain severity in FM patients.

Notes

Acknowledgments

We thank Lena Nordeman, Åsa Cider, Gunilla Jonsson, Annie Palstam and members of the GAU-study group for recruiting, examining or supervising the subjects. This work has been funded by grants from the Swedish Research Council, the Medical Society of Göteborg, the Swedish Rheumatism Association, the King Gustaf V:s 80-year Foundation, the Wilhelm and Martina Lundgrens Foundation, the Foundation to the Memory of Sigurd and Elsa Golje, Rune and Ulla Amlövs Trust, the University of Göteborg, the Regional agreement on medical training and clinical research between the Western Götaland county council and the University of Göteborg (LUA/ALF), the Health and Medical Care Executive Board of Västra Götaland Region. The funding sources have no involvement in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Conflict of interest

The authors declare that they have no competing interests.

References

  1. 1.
    Bennett RM (2002) Adult growth hormone deficiency in patients with fibromyalgia. Curr Rheumatol Rep 4(4):306–312CrossRefPubMedGoogle Scholar
  2. 2.
    Bennett RM, Cook DM, Clark SR, Burckhardt CS, Campbell SM (1997) Hypothalamic-pituitary-insulin-like growth factor-I axis dysfunction in patients with fibromyalgia. J Rheumatol 24(7):1384–1389PubMedGoogle Scholar
  3. 3.
    Bjersing JL, Dehlin M, Erlandsson M, Bokarewa MI, Mannerkorpi K (2012) Changes in pain and insulin-like growth factor 1 in fibromyalgia during exercise: the involvement of cerebrospinal inflammatory factors and neuropeptides. Arthritis Res Ther 14(4):R162. doi:10.1186/ar3902 CrossRefPubMedCentralPubMedGoogle Scholar
  4. 4.
    Bjersing JL, Erlandsson M, Bokarewa MI, Mannerkorpi K (2013) Exercise and obesity in fibromyalgia. Beneficial roles of insulin-like growth factor 1 and resistin? Arthritis Res Ther 15:R34CrossRefPubMedCentralPubMedGoogle Scholar
  5. 5.
    Theadom A, Cropley M, Humphrey KL (2007) Exploring the role of sleep and coping in quality of life in fibromyalgia. J Psychosom Res 62(2):145–151. doi:10.1016/j.jpsychores.2006.09.013 CrossRefPubMedGoogle Scholar
  6. 6.
    Paul-Savoie E, Marchand S, Morin M, Bourgault P, Brissette N, Rattanavong V, Cloutier C, Bissonnette A, Potvin S (2012) Is the deficit in pain inhibition in fibromyalgia influenced by sleep impairments? Open Rheumatol J 6:296–302. doi:10.2174/1874312901206010296 CrossRefPubMedCentralPubMedGoogle Scholar
  7. 7.
    Arnold LM, Hudson JI, Hess EV, Ware AE, Fritz DA, Auchenbach MB, Starck LO, Keck PE Jr (2004) Family study of fibromyalgia. Arthritis Rheum 50(3):944–952. doi:10.1002/art.20042 CrossRefPubMedGoogle Scholar
  8. 8.
    Markkula R, Jarvinen P, Leino-Arjas P, Koskenvuo M, Kalso E, Kaprio J (2009) Clustering of symptoms associated with fibromyalgia in a Finnish Twin Cohort. Eur J Pain 13(7):744–750. doi:10.1016/j.ejpain.2008.09.007 CrossRefPubMedGoogle Scholar
  9. 9.
    Lee YH, Choi SJ, Ji JD, Song GG (2012) Candidate gene studies of fibromyalgia: a systematic review and meta-analysis. Rheumatol Int 32(2):417–426. doi:10.1007/s00296-010-1678-9 CrossRefPubMedGoogle Scholar
  10. 10.
    Smith SB, Maixner DW, Fillingim RB, Slade G, Gracely RH, Ambrose K, Zaykin DV, Hyde C, John S, Tan K, Maixner W, Diatchenko L (2012) Large candidate gene association study reveals genetic risk factors and therapeutic targets for fibromyalgia. Arthritis Rheum 64(2):584–593. doi:10.1002/art.33338 CrossRefPubMedCentralPubMedGoogle Scholar
  11. 11.
    Denk F, McMahon SB (2012) Chronic pain: emerging evidence for the involvement of epigenetics. Neuron 73(3):435–444. doi:10.1016/j.neuron.2012.01.012 CrossRefPubMedCentralPubMedGoogle Scholar
  12. 12.
    Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120(1):15–20. doi:10.1016/j.cell.2004.12.035 CrossRefPubMedGoogle Scholar
  13. 13.
    Baek D, Villen J, Shin C, Camargo FD, Gygi SP, Bartel DP (2008) The impact of microRNAs on protein output. Nature 455(7209):64–71. doi:10.1038/nature07242 CrossRefPubMedCentralPubMedGoogle Scholar
  14. 14.
    Zhu H, Fan GC (2011) Extracellular/circulating microRNAs and their potential role in cardiovascular disease. Am J Cardiovasc Dis 1(2):138–149PubMedCentralPubMedGoogle Scholar
  15. 15.
    Rayner KJ, Hennessy EJ (2013) Extracellular communication via microRNA: lipid Particles have a new message. J Lipid Res. doi:10.1194/jlr.R034991 Google Scholar
  16. 16.
    Nana-Sinkam SP, Croce CM (2013) Clinical applications for microRNAs in cancer. Clin Pharmacol Ther 93(1):98–104. doi:10.1038/clpt.2012.192 CrossRefPubMedGoogle Scholar
  17. 17.
    van Rooij E, Olson EN (2012) MicroRNA therapeutics for cardiovascular disease: opportunities and obstacles. Nat Rev Drug Discovery 11(11):860–872. doi:10.1038/nrd3864 CrossRefGoogle Scholar
  18. 18.
    Bjersing JL, Lundborg C, Bokarewa MI, Mannerkorpi K (2013) Profile of Cerebrospinal microRNAs in Fibromyalgia. PLoS One 8(10):e78762. doi:10.1371/journal.pone.0078762 CrossRefPubMedCentralPubMedGoogle Scholar
  19. 19.
    Mannerkorpi K, Nordeman L, Cider A, Jonsson G (2010) Does moderate-to-high intensity Nordic walking improve functional capacity and pain in fibromyalgia? A prospective randomized controlled trial. Arthritis Res Ther 12(5):R189. doi:10.1186/ar3159 CrossRefPubMedCentralPubMedGoogle Scholar
  20. 20.
    Wolfe F, Smythe HA, Yunus MB, Bennett RM, Bombardier C, Goldenberg DL, Tugwell P, Campbell SM, Abeles M, Clark P et al (1990) The American college of rheumatology 1990 criteria for the classification of fibromyalgia. Report of the multicenter criteria committee. Arthritis Rheum 33(2):160–172CrossRefPubMedGoogle Scholar
  21. 21.
    Caman OK, Calling S, Midlov P, Sundquist J, Sundquist K, Johansson SE (2013) Longitudinal age-and cohort trends in body mass index in Sweden: a 24-year follow-up study. BMC Public Health 13:893. doi:10.1186/1471-2458-13-893 CrossRefPubMedCentralPubMedGoogle Scholar
  22. 22.
    Kosek E, Ekholm J, Nordemar R (1993) A comparison of pressure pain thresholds in different tissues and body regions. Long-term reliability of pressure algometry in healthy volunteers. Scand J Rehabil Med 25(3):117–124PubMedGoogle Scholar
  23. 23.
    Burckhardt CS, Clark SR, Bennett RM (1991) The fibromyalgia impact questionnaire: development and validation. J Rheumatol 18(5):728–733PubMedGoogle Scholar
  24. 24.
    Ericsson A, Mannerkorpi K (2007) Assessment of fatigue in patients with fibromyalgia and chronic widespread pain. Reliability and validity of the Swedish version of the MFI-20. Disabil Rehabil 29(22):1665–1670. doi:10.1080/09638280601055782 CrossRefPubMedGoogle Scholar
  25. 25.
    Akerstedt T, Knutsson A, Westerholm P, Theorell T, Alfredsson L, Kecklund G (2002) Sleep disturbances, work stress and work hours: a cross-sectional study. J Psychosom Res 53(3):741–748CrossRefPubMedGoogle Scholar
  26. 26.
    Jensen SG, Lamy P, Rasmussen MH, Ostenfeld MS, Dyrskjot L, Orntoft TF, Andersen CL (2011) Evaluation of two commercial global miRNA expression profiling platforms for detection of less abundant miRNAs. BMC Genom 12:435. doi:10.1186/1471-2164-12-435 CrossRefGoogle Scholar
  27. 27.
    Qiu X, Wu H, Hu R (2013) The impact of quantile and rank normalization procedures on the testing power of gene differential expression analysis. BMC Bioinformatics 14(1):124. doi:10.1186/1471-2105-14-124 CrossRefPubMedCentralPubMedGoogle Scholar
  28. 28.
    White RE, Giffard RG (2012) MicroRNA-320 induces neurite outgrowth by targeting ARPP-19. NeuroReport 23(10):590–595. doi:10.1097/WNR.0b013e3283540394 CrossRefPubMedCentralPubMedGoogle Scholar
  29. 29.
    Sanchez Freire V, Burkhard FC, Kessler TM, Kuhn A, Draeger A, Monastyrskaya K (2010) MicroRNAs may mediate the down-regulation of neurokinin-1 receptor in chronic bladder pain syndrome. Am J Pathol 176(1):288–303. doi:10.2353/ajpath.2010.090552 CrossRefPubMedCentralPubMedGoogle Scholar
  30. 30.
    Orlova IA, Alexander GM, Qureshi RA, Sacan A, Graziano A, Barrett JE, Schwartzman RJ, Ajit SK (2011) MicroRNA modulation in complex regional pain syndrome. J Transl Med 9:195. doi:10.1186/1479-5876-9-195 CrossRefPubMedCentralPubMedGoogle Scholar
  31. 31.
    Roush S, Slack FJ (2008) The let-7 family of microRNAs. Trends Cell Biol 18(10):505–516. doi:10.1016/j.tcb.2008.07.007 CrossRefPubMedGoogle Scholar
  32. 32.
    Nair VS, Maeda LS, Ioannidis JP (2012) Clinical outcome prediction by microRNAs in human cancer: a systematic review. J Natl Cancer Inst 104(7):528–540. doi:10.1093/jnci/djs027 CrossRefPubMedCentralPubMedGoogle Scholar
  33. 33.
    He Y, Yang C, Kirkmire CM, Wang ZJ (2010) Regulation of opioid tolerance by let-7 family microRNA targeting the mu opioid receptor. J Neurosci 30(30):10251–10258. doi:10.1523/JNEUROSCI.2419-10.2010 CrossRefPubMedCentralPubMedGoogle Scholar
  34. 34.
    Tobon KE, Chang D, Kuzhikandathil EV (2012) MicroRNA 142-3p mediates post-transcriptional regulation of D1 dopamine receptor expression. PLoS One 7(11):e49288. doi:10.1371/journal.pone.0049288 CrossRefPubMedCentralPubMedGoogle Scholar
  35. 35.
    Tan X, Zhang P, Zhou L, Yin B, Pan H, Peng X (2012) Clock-controlled mir-142-3p can target its activator, Bmal1. BMC Mol Biol 13:27. doi:10.1186/1471-2199-13-27 CrossRefPubMedCentralPubMedGoogle Scholar
  36. 36.
    Tanaka M, Yamaguchi E, Takahashi M, Hashimura K, Shibata T, Nakamura W, Nakamura TJ (2012) Effects of age-related dopaminergic neuron loss in the substantia nigra on the circadian rhythms of locomotor activity in mice. Neurosci Res 74(3–4):210–215. doi:10.1016/j.neures.2012.09.005 CrossRefPubMedGoogle Scholar
  37. 37.
    Revel FG, Moreau JL, Gainetdinov RR, Bradaia A, Sotnikova TD, Mory R, Durkin S, Zbinden KG, Norcross R, Meyer CA, Metzler V, Chaboz S, Ozmen L, Trube G, Pouzet B, Bettler B, Caron MG, Wettstein JG, Hoener MC (2011) TAAR1 activation modulates monoaminergic neurotransmission, preventing hyperdopaminergic and hypoglutamatergic activity. Proc Natl Acad Sci USA 108(20):8485–8490. doi:10.1073/pnas.1103029108 CrossRefPubMedCentralPubMedGoogle Scholar
  38. 38.
    Ablin JN, Bar-Shira A, Yaron M, Orr-Urtreger A (2009) Candidate-gene approach in fibromyalgia syndrome: association analysis of the genes encoding substance P receptor, dopamine transporter and alpha1-antitrypsin. Clin Exp Rheumatol 27(5 Suppl 56):S33–S38PubMedGoogle Scholar
  39. 39.
    Potvin S, Larouche A, Normand E, de Souza JB, Gaumond I, Grignon S, Marchand S (2009) DRD3 Ser9Gly polymorphism is related to thermal pain perception and modulation in chronic widespread pain patients and healthy controls. J Pain 10(9):969–975. doi:10.1016/j.jpain.2009.03.013 CrossRefPubMedGoogle Scholar
  40. 40.
    Buskila D, Cohen H, Neumann L, Ebstein RP (2004) An association between fibromyalgia and the dopamine D4 receptor exon III repeat polymorphism and relationship to novelty seeking personality traits. Mol Psychiatry 9(8):730–731. doi:10.1038/sj.mp.4001506 CrossRefPubMedGoogle Scholar
  41. 41.
    Finnerty JR, Wang WX, Hebert SS, Wilfred BR, Mao G, Nelson PT (2010) The miR-15/107 group of microRNA genes: evolutionary biology, cellular functions, and roles in human diseases. J Mol Biol 402(3):491–509. doi:10.1016/j.jmb.2010.07.051 CrossRefPubMedCentralPubMedGoogle Scholar
  42. 42.
    Yu D, Zhou H, Xun Q, Xu X, Ling J, Hu Y (2012) microRNA-103 regulates the growth and invasion of endometrial cancer cells through the downregulation of tissue inhibitor of metalloproteinase 3. Oncol Lett 3(6):1221–1226. doi:10.3892/ol.2012.638 PubMedCentralPubMedGoogle Scholar
  43. 43.
    Trajkovski M, Hausser J, Soutschek J, Bhat B, Akin A, Zavolan M, Heim MH, Stoffel M (2011) MicroRNAs 103 and 107 regulate insulin sensitivity. Nature 474(7353):649–653. doi:10.1038/nature10112 CrossRefPubMedGoogle Scholar
  44. 44.
    Herrera BM, Lockstone HE, Taylor JM, Ria M, Barrett A, Collins S, Kaisaki P, Argoud K, Fernandez C, Travers ME, Grew JP, Randall JC, Gloyn AL, Gauguier D, McCarthy MI, Lindgren CM (2010) Global microRNA expression profiles in insulin target tissues in a spontaneous rat model of type 2 diabetes. Diabetologia 53(6):1099–1109. doi:10.1007/s00125-010-1667-2 CrossRefPubMedCentralPubMedGoogle Scholar
  45. 45.
    Foley NH, O’Neill LA (2012) miR-107: a toll-like receptor-regulated miRNA dysregulated in obesity and type II diabetes. J Leukoc Biol 92(3):521–527. doi:10.1189/jlb.0312160 CrossRefPubMedGoogle Scholar
  46. 46.
    Pan S, Zheng Y, Zhao R, Yang X (2013) miRNA-374 regulates dexamethasone-induced differentiation of primary cultures of porcine adipocytes. Hormone and metabolic research = Hormon-und Stoffwechselforschung = Hormones et metabolisme. doi:10.1055/s-0033-1334896
  47. 47.
    Noren Hooten N, Abdelmohsen K, Gorospe M, Ejiogu N, Zonderman AB, Evans MK (2010) microRNA expression patterns reveal differential expression of target genes with age. PLoS One 5(5):e10724. doi:10.1371/journal.pone.0010724 CrossRefPubMedCentralPubMedGoogle Scholar
  48. 48.
    Wang WX, Huang Q, Hu Y, Stromberg AJ, Nelson PT (2011) Patterns of microRNA expression in normal and early Alzheimer’s disease human temporal cortex: white matter versus gray matter. Acta Neuropathol 121(2):193–205. doi:10.1007/s00401-010-0756-0 CrossRefPubMedCentralPubMedGoogle Scholar
  49. 49.
    Wang WX, Rajeev BW, Stromberg AJ, Ren N, Tang G, Huang Q, Rigoutsos I, Nelson PT (2008) The expression of microRNA miR-107 decreases early in Alzheimer’s disease and may accelerate disease progression through regulation of beta-site amyloid precursor protein-cleaving enzyme 1. J Neurosci 28(5):1213–1223. doi:10.1523/JNEUROSCI.5065-07.2008 CrossRefPubMedCentralPubMedGoogle Scholar
  50. 50.
    Brenu EW, Ashton KJ, van Driel M, Staines DR, Peterson D, Atkinson GM, Marshall-Gradisnik SM (2012) Cytotoxic lymphocyte microRNAs as prospective biomarkers for Chronic Fatigue Syndrome/Myalgic Encephalomyelitis. J Affect Disord 141(2–3):261–269. doi:10.1016/j.jad.2012.03.037 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Jan L. Bjersing
    • 1
  • Maria I. Bokarewa
    • 1
  • Kaisa Mannerkorpi
    • 1
    • 2
  1. 1.Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska AcademyUniversity of GothenburgGöteborgSweden
  2. 2.Sahlgrenska AcademyUniversity of Gothenburg Centre for Person-centred Care (GPCC)GöteborgSweden

Personalised recommendations