Next Article in Journal
Evaluation of a Methylcellulose and Hyaluronic Acid Hydrogel as a Vehicle for Rectal Delivery of Biologics
Next Article in Special Issue
DPYD and Fluorouracil-Based Chemotherapy: Mini Review and Case Report
Previous Article in Journal
Spatially Specific Liposomal Cancer Therapy Triggered by Clinical External Sources of Energy
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Pharmaceutics
Volume 11
Issue 3
10.3390/pharmaceutics11030126
pharmaceutics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Communication

Impact of Polypharmacy on Candidate Biomarker miRNomes for the Diagnosis of Fibromyalgia and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Striking Back on Treatments

by
Eloy Almenar-Pérez
1,
Teresa Sánchez-Fito
1,
Tamara Ovejero
2,
Lubov Nathanson
3,4 and
Elisa Oltra
2,5,*
1
Escuela de Doctorado, Universidad Católica de Valencia San Vicente Mártir, 46001 Valencia, Spain
2
School of Medicine, Universidad Católica de Valencia San Vicente Mártir, 46001 Valencia, Spain
3
Kiran C Patel College of Osteopathic Medicine, Nova Southeastern University, Ft Lauderdale, FL 33314, USA
4
Institute for Neuro Immune Medicine, Nova Southeastern University, Ft Lauderdale, FL 33314, USA
5
Unidad Mixta CIPF-UCV, Centro de Investigación Príncipe Felipe, 46012 Valencia, Spain
*
Author to whom correspondence should be addressed.
Pharmaceutics 2019, 11(3), 126; https://doi.org/10.3390/pharmaceutics11030126
Submission received: 15 January 2019 / Revised: 26 February 2019 / Accepted: 5 March 2019 / Published: 18 March 2019
(This article belongs to the Special Issue Precision Medicine: Applied Concepts of Pharmacogenomics in Patients with Various Diseases and Polypharmacy)
Versions Notes

Abstract

:
Fibromyalgia (FM) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are diseases of unknown etiology presenting complex and often overlapping symptomatology. Despite promising advances on the study of miRNomes of these diseases, no validated molecular diagnostic biomarker yet exists. Since FM and ME/CFS patient treatments commonly include polypharmacy, it is of concern that biomarker miRNAs are masked by drug interactions. Aiming at discriminating between drug-effects and true disease-associated differential miRNA expression, we evaluated the potential impact of commonly prescribed drugs on disease miRNomes, as reported by the literature. By using the web search tools SM2miR, Pharmaco-miR, and repoDB, we found a list of commonly prescribed drugs that impact FM and ME/CFS miRNomes and therefore could be interfering in the process of biomarker discovery. On another end, disease-associated miRNomes may incline a patient’s response to treatment and toxicity. Here, we explored treatments for diseases in general that could be affected by FM and ME/CFS miRNomes, finding a long list of them, including treatments for lymphoma, a type of cancer affecting ME/CFS patients at a higher rate than healthy population. We conclude that FM and ME/CFS miRNomes could help refine pharmacogenomic/pharmacoepigenomic analysis to elevate future personalized medicine and precision medicine programs in the clinic.

1. Introduction

Fibromyalgia (FM) is a debilitating disorder characterized by a low pain threshold and muscle tenderness accompanied by bowel abnormalities, sleep disturbances, depressive episodes, cognitive problems, and chronic pain [1,2,3,4]. Though commonly comorbid with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), a disease also showing a complex clinical pathophysiology [5,6,7,8,9,10,11], these syndromes have been classified by the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), with separate codes (M79.7 and R53.82 or G93.3 if post-viral, for FM and ME/CFS, respectively) [12]. However, disease distinctions remain under debate [5].
Although possibly underestimated, the global prevalence for FM has been set at 2–8% and at 0.23–0.41 for ME/CFS with predominant ratios of females over males [13,14,15,16,17]. In addition, increasing numbers of patients being affected at early ages [18] highlights the considerable and raising needs for appropriate healthcare programs and the stepping demands for the alleviation of associated economic/social burdens.
Post-exertional malaise (PEM), a clinical hallmark of ME/CFS, together with additional clinical and biological parameters differing between these two diseases [19,20,21,22,23,24] seem to support a distinct underlying pathophysiology and possibly etiology for FM and ME/CFS. Aimed at clarifying this diagnostic conflict through an improved understanding of the biology of disease onset and evolution, some research groups, ours included, have set out to identify molecular biomarkers of these illnesses [25].
MicroRNAs or miRs constitute attractive candidates for the diagnosis of FM and ME/CFS, as they have been found to associate with the disease state of other complex chronic diseases [26,27] and may even be used to measure disease stage and response to treatments [28]. In their mature form (20–22 nts), they epigenetically control gene expression by directing particular sets of mRNAs, presenting partial complementation in their 3’UTRs, to degradation [29]. Other regulatory mechanisms have also been linked to the activity of these small molecules [30].
In addition to their biomarker value, miRNAs could potentially be targeted by small drugs, either directly through the binding of chemical compounds to particular grooves or pockets of their secondary structures, in their mature or precursor forms, as isolated or complexed molecules, or indirectly by interfering with proteins involved in their biogenesis or recycling, including regulation of transcription factors driving miRNA synthesis [31,32,33,34]. Therefore, directional FM and ME/CFS treatments based on miRNA targeting strategies are envisioned as potential curative therapies by themselves or as co-adjuvants in the near future.
MiRNA capacity to sense and respond to environmental cues [35,36,37], however, makes the establishment of correlations between particular disease states and miRNA profile changes challenging. To minimize potential environmental confounding factors, healthy participants are often population-matched by sex, age, and quite frequently BMI (body mass index) with the participating patient group. Careful selection of participants and proper study design are key factors in identifying miRNA disease-associated profiles (disease miRNomes), as miRNA levels also change in response to hormone challenges, during aging and metabolic states [38,39,40,41], including the post-prandial estate [42]. In the context of FM and ME/CFS, since miRNomes change with exercise [43], inclusion of sedentary control groups would be desirable.
Current treatments of both FM and ME/CFS diseases are symptom-palliative only [44,45,46,47,48]. Due to multi-symptomatology, patient prescriptions frequently involve polypharmacy, which may significantly impact downstream molecular analysis of the disease. With this perspective, a recent joint initiative worked out by the NINDS (National Institute of Neurological Disorders and Stroke) at the NIH (National Institute of Health) in Bethesda, MD (USA) and other federal agencies has made available case report forms (CRFs) and guidelines to register drug use in ME/CFS studies [49]. The ME/CFS Common Data Elements initiative or CDE Project aimed at standardizing clinical relevant variables for the study of ME/CFS covers various areas organized by domains and sub-domains. Information is publicly available at the NINDS Common Data Elements web page [49].
A recently observed feature of miRNAs is their role in determining drug efficacy [50,51]. The traditional field of pharmacogenomics dealing with how individual genomic features, including SNPs (single nucleotide polymorphisms) and CNVs (copy number variants), influence a patient’s response to drug-based treatments and sensitivity to toxic effects is becoming complemented by individual epigenetic profiles including alternative splicing events and miRNomes (pharmacoepigenomics), with the aim of elevating predictions of the most effective and safest options towards improved personalized treatments in the clinic [52,53,54,55]. In addition to epigenetic regulation of drug targets, regulation of genes related to drug absorption, distribution, metabolism, and excretion (ADME) may translate into significant inter-individual differences to drug response [56]. In this context, it should be of relevance to take into account a patient’s FM or/and ME/CFS condition when standardized treatments for diseases other than FM and ME/CFS are in need. In particular, FM and ME/CFS associated miRNA profiles might promote drug efficacy or inhibit drug function when compared to non-FM and non-ME/CFS patients and consequently impact or influence an FM and ME/CFS patient’s response to pharmacological treatments or sensitivity to adverse reactions. Interestingly, and in line with this, FM and ME/CFS patients report suffering from multiple chemical sensitivity [57].
In this paper, we have interrogated the potential impact of commonly prescribed drugs to treat FM and ME/CFS on miRNA profiles in an effort to discern between miRNAs potentially linked to disease from those that might be a consequence of drug intake. We have also evaluated miRNA–target gene–drug interactions of differentially expressed (DE) miRNAs in FM and ME/CFS as an approach to determine the ability or predisposition of these patients to respond to common clinical treatments for diseases in general, including diseases other than FM and ME/CFS, which may appear comorbid at some point in FM and ME/CFS patients’ lives.

2. Materials and Methods

2.1. Study Search

To locate experimental work aimed at studying miRNA profiles in FM or ME/CFS, a bibliographic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria [58] was performed using Pubmed and Web of Science databases [59,60] up to January 2019. The search terms used in “all fields” included: “fibromyalgia” AND “microRNA” OR “miR” on one search, and “chronic fatigue syndrome” AND “CFS” in combination with “microRNA” OR “miR” in another. The use of the term “myalgic encephalomyelitis” to describe the disease in other searches did not yield any additional experimental publications in the field. The trial Pubmed Labs tool, including article snippets and other improvements was also used in the search [61]. Manual curation to filter out non-experimental or unrelated hits was applied.
For compounds commonly prescribed to treat symptoms in FM and ME/CFS, a search in the Cochrane library and Pubmed databases [59,62] was performed using as search terms either “fibromyalgia systematic review” AND “drug,” “chronic fatigue syndrome” AND “CFS systematic review” AND “drug,” or “myalgic encephalomyelitis systematic review” AND “drug.” Most recently updated reviews were adopted as reference manuscripts.

2.2. Identification of miRNA–Drug–Disease Interactions

Features of miRNA and drug understudies, International Union of Pure and Applied Chemistry (IUPAC) names included, were found in miRBase and Drugbank databases, respectively [63,64]. FM and ME/CFS miRNomes were evaluated for miRNA–drug interactions using either SM2miR or Pharmaco-miR web search tools [50,65]. To find potential miRNomes derived from patient polypharmacy, SM2miR output was filtered using as criteria “drugs commonly prescribed to treat FM and ME/CFS symptoms,” as described in the previous Section 2.1. Treatments to disease to which FM and ME/CFS patients may respond differently from non-FM and non-ME/CFS populations were spotted by searching the repoDB database [66] with the Pharmaco-miR drug hits obtained with FM or ME/CFS miRNome searches.

3. Results

3.1. miRNomes Associating with the Studied Diseases

3.1.1. miRNomes of FM

By reviewing the literature, as described in Methods, we found five studies reporting differential expression (DE) of particular miRNAs in FM patients with respect to healthy populations using multiplex approaches, either microarrays or RT-qPCR panels (Table 1 and Table S1). One of them measured miRNA levels in cerebrospinal fluid (CSF) [67], while the rest evaluated them in blood fractions [68,69,70,71]—two used white blood cells [69,71] and two analyzed serum [68,70].
According to these reports, a total of 85 FM patients and 86 healthy participants were screened for differential miRNA expression, and little coincidence was found (only 9 miRNAs reported by more than one study) (Table 1, miRNAs in bold) even within the same blood fraction type and in spite of using common diagnostic criteria (ACR 1990). Gene Ontology (GO) analysis, however, more commonly showed metabolic and neural pathways associating to DE miRNAs, indicating common cellular pathways affected by different FM miRNomes.

3.1.2. miRNomes of ME/CFS

A similar bibliographic search to the one performed in FM (Section 3.1.1 of this manuscript) yielded, after filtering out unrelated, gene-focused studies, only three studies showing DE of miRNAs in ME/CFS at basal levels, yet, they included a similar total number of patients and controls (83 and 47, respectively) (Table 2) [72,73,74]. It should be noted that an additional multiplex miRNA study evaluating the DE of miRNAs in ME/CFS upon an exercise challenge was excluded on the basis of reporting no basal disease miRNomes [75]. Again, as in FM studies, little overlap of DE miRNAs could be found across ME/CFS studies (only 4 miRNAs were reported by more than one ME/CFS study, bold miRNAs in Table 2). In this case, this could be somehow expected as blood fractions and diagnostic criteria varied across studies. In fact, only the most recent study by Petty et al. included the more restrictive Canadian criteria for patient selection [74]. Nevertheless, once more, a coincidence of mainly affected GO terms was found, indicating major immune defects in ME/CFS through different miRNomes.
Surprisingly as many as 19 miRNAs were found to be commonly reported as DE by FM and ME/CFS studies, the significance of which is unknown at present (miRNAs underlined in Table 1 and Table 2).

3.2. Polypharmacy Potentially Impacting miRNA Profiles

As mentioned above, our general aims included determining drug–miRNA and drug–disease interactions in the context of FM, or ME/CFS miRNomes, for the purpose of identifying potential interference of drugs in miRNA profiling, which could bias research outcomes on one hand and, on the other, determine whether disease miRNA profiles could influence drug response in these patients. This section focuses on selecting drugs commonly prescribed to FM and ME/CFS patients to evaluate the effect that polypharmacy might have on miRNomes of these diseases.

3.2.1. Polypharmacy in FM

Based on the recent Cochrane report by Häuser et al. [44], drugs that have been commonly used to treat FM in the clinical practice can be classified into the following six classes: antidepressants, antiepileptics, antipsychotics, cannabinoids, nonsteroidal anti-inflammatory drugs (NSAIDs), and opioids. Rather than analyzing the quality of evidence of clinical trials using these substances, we were interested in assigning the active principle and IUPAC names to the reported compounds, to facilitate our downstream analysis (Table 3). Additional literature supporting the use of compounds for each of the six classes described by Häuser et al. to treat FM patients is provided in Table 3 [76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93].

3.2.2. Polypharmacy in ME/CFS

Opposite to FM, no drug-based Cochrane review for the treatment of ME/CFS could be found. The three hits obtained by using the MeSH search terms “chronic fatigue syndrome” were reviews on exercise, CBT (cognitive behaviour therapy) and Chinese herbs [94,95,96]. Therefore, we decided to use the recent reviews by Collatz et al. and Smith et al. as reference papers to evaluate common drug-based ME/CFS therapies [46,47]. Additional bibliography supporting the use of polypharmacy in ME/CFS was also included [46,48,97,98,99,100,101,102,103,104,105]. Similar to what has been described in Section 3.2.1, a documented summary of drugs commonly prescribed to ME/CFS patients that could impact miRNA screenings is shown in Table 4 together with active principles and IUPAC names.
Although possibly not complete, Table 3 and Table 4 include the most representative compounds to treat FM and ME/CFS according to the consulted authors [44,48,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,97,98,99,100,101,102,103,104,105]. Unexpectedly, a single IUPAC overlap, corresponding to the selective serotonin reuptake inhibitor (SSRI) fluoxetine, was found for drugs commonly prescribed for FM and ME/CFS (in bold in Table 3 and Table 4), indicating little prescription overlap at the IUPAC name level despite both groups of patients presenting common symptomatology. Special attention should be placed to common prescriptions as they may more readily allow for identifying the effects of drugs on miRNA levels over disease-related changes.

3.3. miRNA–Drug Interactions in FM and ME/CFS

With the intention to discriminate whether the miRNomes proposed to associate with FM or with ME/CFS are derived from drug intake differences between the patient and control groups, we performed a screen of drugs that could alter any of the miRNAs in these miRNomes using the SM2miR web server [65] and each of the individual DE miRNAs or disease miRNome as the input, as previously detailed in the Methods section.
The SM2miR drug output file (Table S2) was contrasted with the FM and ME/CFS polypharmacy tables (Table 3 and Table 4), and it was found that five of the commonly prescribed drugs for FM or ME/CFS (DHA, fluoxetine, glucocorticoids, morphine, and valproate) are estimated to alter the levels of one or more of the miRNAs found DE in FM or ME/CFS screenings (potential disease-associated miRNomes) and therefore these drugs could constitute confounding variables of the assay (Table 5) [106,107,108,109,110,111]. Overlapping tendencies may suggest that the detected differences between studied groups associate with treatment rather than constituting potential biomarkers of disease, while opposed tendencies might reflect additional factors leading to differential expression other than drug intake, disease status included. Importantly, as summarized in Table 5, the expression of miRNA-27b reported in miRNomes of both FM and ME/CFS in more than one report [67,74] is affected by the only overlapping compound commonly prescribed for treatment of both diseases (fluoxetine), indicating a potential drug–interference effect. Three additional miRNAs reported as miRNomes of ME/CFS by more than one study (miR-26a, miR-126, and miR-191) are also affected by drugs frequently prescribed to ME/CFS patients, so special attention should be paid when interpreting miRNome results including these miRNAs.
It must be pointed out that, in an effort to complete the search as much as possible, the list of DE miRNAs in FM and ME/CFS used in the SM2miR search not only included the miRNAs listed in Table 1 and Table 2 but also those documented in the supplementary tables of the listed literature [67,68,69,70,71,72,73,74].

3.4. Drug–Disease Interactions Based on FM and ME/CFS miRNomes

To evaluate potential biased responses of FM and ME/CFS patients to pharmacotherapy in general, due to their DE miRNA profiles, we searched for diseases commonly treated with small-molecule drugs that depend on gene sets linked to FM or ME/CFS miRNomes (miRNA–gene–drug datasets). With this purpose, individual DE miRNAs in FM or ME/CFS were used as input in the Pharmaco-miR web search tool [50]. The output constituted a list of genes whose expression is dependent on FM and ME/CFS DE miRNAs (Table S3) and a third column facilitating small molecule drug associations for these gene lists. Among the 709 small molecules linked to FM miRNome, only 595 appeared registered in the Drugbank database. Out of the 668 small molecules associating with ME/CFS miRNomes, 557 appeared registered in Drugbank [64].
Finally, Drugbank numbers of these small molecules were used as the input to search repoDB, a database of small drugs developed by Brown and Patel to facilitate screenings for drug repositioning [66]. The results (Table S3, miRNome–drug–disease tabs) show 1480 and 1455 diseases treated with small molecules, respectively, associating with FM or ME/CFS miRNomes after filtering out duplications. Out of these diseases potentially impacting individualized medicine programs for FM and ME/CFS patients, more than 30% corresponded to cancer of some type. Within cancer, 13% corresponded to lymphoma, and 14% to lymphoma plus leukemia. This seems to indicate that quite possibly FM and ME/CFS patients may respond differently to treatments for these diseases with respect to non-FM and non-ME/CFS patients, so it is advised that attention be paid to individualized medicine programs for the treatment of these cancers in the case of FM and CFS/ME patients.

4. Discussion

This paper is the first to evaluate the relationship between commonly prescribed drugs for FM and ME/CFS and miRNA expression and compares these profiles to FM- and ME/CFS-reported miRNomes in an effort to discern miRNAs presenting differential expression due to medication from differences more likely related to disease. The resources used in this study are limited and therefore it is expected that the evidence presented here will be refined as more data becomes available. The topic is not exclusive to FM and ME/CFS, as it can be extended to any other study evaluating miRNomes associated with disease. However, the fact that FM and ME/CFS patients are usually polymedicated to palliate the multiple symptoms that associate with these illnesses extends this concern to higher levels, particularly demanding careful registry of study participants’ medication, when restrictive medication inclusion criteria is not an option. In this sense, the ME/CFS Common Data Elements initiative [49] has made publicly available medication guidelines and CRFs at the disposition of researchers, which may help standardize medication registry in ME/CFS studies.
Although some researchers have expressed their concern of the impact of drug use by FM and ME/CFS patients on the study of molecular markers and although recent work in the area is already reporting the medication used by participants [71,112], the information of registered drugs is not yet used to evaluate potential interference or bias of results. To elevate biomarker screenings of FM and ME/CFS based on miRNA profiles, complex stratified analysis to filter out potential drug and other confounding variables will be required. The complexity and limitations of this analysis is served by the fact that miRNA expression responds to many cues, such as exercise and diet, hormones, sex, and aging [38,39,40,41,42,43].
A commonly used approach to minimize confounding variables, although not free of certain difficulties for sampling, is to set restrictive inclusion criteria including sex selection, narrow age range, and BMI. This is important in miRNA screenings as these parameters are known to affect miRNA profiles [113]. Additional sampling details such as fasting blood draw and the selection of sedentary healthy controls may improve study outcomes. Some authors have even taken into account time of blood collection to reduce circadian variation [71], but it may not be possible to eliminate polypharmacy, particularly in studies including severely affected FM and ME/CFS patients.
Prescriptions for other common health problems such as diabetes or high cholesterol, diet supplements and some recreational drugs alter the expression of some miRNAs in FM and ME/CFS miRNomes (Table S4) [107,114,115,116,117,118,119,120,121,122,123,124,125]. Hormones and other natural compounds also impact FM and ME/CFS miRNomes (Table S5) [106,126,127,128,129,130,131,132,133,134,135,136], stressing the necessity for researchers to collect complete medical histories of participants to accurately evaluate miRNAs as biomarkers of these diseases.
Though FM and ME/CFS miRNomes relate to disease or derive from chronic polypharmacy use, DE miRs should represent a relevant factor to take into account when treatments for other diseases such as cancer are due. Here, we performed an analysis of the diseases whose treatment response could differ in the context of FM and ME/CFS miRNomes, and found a broad range of them. The major representation of cancer (above 35%) might merely reflect the fact that more studies are registered in the field, biasing databases. Importantly, a relevant number of hits associated with lymphoma, a type of cancer appearing at higher incidence among ME/CFS patients [137], is possibly due to associated immune dysfunctions of this disease.
Personalized medicine programs considering miRNome backgrounds may more adequately select effective treatments with reduced side effects. It is therefore envisioned that future improved therapeutic analysis, including pharmacogenomics and pharmacoepigenomics (precision medicine programs), will rely on complex software tools fed with large datasets. Further miRNA profiling studies including a larger number of samples are required to build on the scarce available FM and ME/CFS miRNome data. Since technical variability in miRNA qPCR replicates has been documented, with TaqMan overweighing qScript PCR [138], future studies should include repeated independent measures or either use alternative enzyme-free approaches such as NanoString [139].
In general, we have evaluated the effects of polypharmacy and miRNomes at individual levels, meaning that the information obtained here corresponds to the effects of a single drug on DE miRNAs or the impact of an individual miRNA on drug performance, but the effects of combined therapies on miRNA profiles or sets of DE miRNAs on drug response may not replicate or be additive of single events, highlighting the limitation of our study. In addition, most molecular data come from analysis of blood or other body fluid samples and more sparingly from non-cancerous solid tissues, limiting the validity of our results, as miRNA profiles are known to be tissue-restricted [140]. Drug assays are performed in either animal models or tumor cell lines leading to results that may not replicate in other systems, especially since many miRNAs are primate or human-specific [63,141].
In summary, as larger data sets become available to nurture databanks, biomarker discovery will be facilitated and personalized medicine programs will be refined, upgrading current diagnostic tools and clinical treatments. Drug–transcriptome interactions are key factors in either context, particularly in diseases subject to polypharmacy such as FM and ME/CFS.

5. Conclusions

The analysis presented here seem to support a potential impact of FM and ME/CFS polypharmacy in the discovery of miRNomes associating with these diseases. Based on this possibility, caution is advised when designing studies aimed at determining DE miRNAs linked to these diseases, including complete drug registry to permit stratified analysis.
FM and ME/CFS miRNomes may predispose patients to respond differently to a large variety of drug-based treatments, including those used for a large number of cancers, highlighting the importance of considering this epigenomic bias in refined personalized programs towards improving a patient’s response to clinical treatments while minimizing toxicity. It is estimated that more sophisticated informatic tools will help with these predictions, but the paucity of molecular studies in FM and ME/CFS currently limits their development.
The results presented here are not definitive at this stage, but their observations should stimulate additional studies to further explore miRNA–drug interactions in the context of FM and ME/CFS.

Supplementary Materials

The following are available online at https://www.mdpi.com/1999-4923/11/3/126/s1. Table S1: PRISMA based search of FM and ME/CFS miRNA profiling studies; Table S2: FM and ME/CFS miRNomes SM2miR-based drug search; Table S3: FM and ME/CFS miRNomes Pharmaco-miR-based gene-drug associations and repoDB-drug-disease screening; Table S4: Effect of additional drugs, supplements and recreational drugs on FM and ME/CFS miRNomes and Table S5: Effect of hormones and other natural compounds on FM and ME/CFS miRNomes.

Author Contributions

Individual author contributions are as follows: conceptualization, E.O.; methodology, E.A.-P.; T.S.-F.; T.O.; L.N., and E.O.; software, formal analysis, and data curation, E.A.-P.; T.S.-F.; T.O.; L.N., and E.O.; writing—original draft preparation, E.O.; writing—review and editing, E.A.-P.; T.S.-F.; T.O.; L.N., and E.O.; funding acquisition, E.O.

Funding

This research was funded by Universidad Católica de Valencia San Vicente Mártir research grant 2018-270-01 to E.O.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

Fibromyalgia (FM); myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS); microRNAs (miRs); differentially expressed (DE); American College of Rheumatology (ACR); gene ontology (GO); Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria (PRISMA); International Classification of Diseases (ICD); non-steroidal anti-inflammatory drugs (NSAIDs); cognitive behaviour therapy (CBT); International Union of Pure and Applied Chemistry (IUPAC); concept unique identifiers (CUIs); national clinical trial (NCT); Common Data Elements (CDE); case report forms (CRFs); National Institute of Health (NIH); absorption, distribution, metabolism, and excretion (ADME); selective serotonin reuptake inhibitor (SSRI); serotonin-norepinephrine reuptake inhibitors (SNRIs); monoamine oxidase inhibitors (MAOIs).

References

  1. Wolfe, F.; Smythe, H.A.; Yunus, M.B.; Bennett, R.M.; Bombardier, C.; Goldenberg, D.L.; Tugwell, P.; Campbell, S.M.; Abeles, M.; Clark, P.; et al. The American College of Rheumatology 1990 criteria for the classification of fibromyalgia. Report of the multicenter criteria committee. Arthritis Rheum. 1990, 33, 160–172. [Google Scholar] [CrossRef] [PubMed]
  2. Wolfe, F.; Clauw, D.J.; Fitzcharles, M.A.; Goldenberg, D.L.; Katz, R.S.; Mease, P.; Russell, A.S.; Russell, I.J.; Winfield, J.B.; Yunus, M.B. The American College of Rheumatology preliminary diagnostic criteria for fibromyalgia and measurement of symptom severity. Arthritis Care Res. (Hoboken) 2010, 62, 600–610. [Google Scholar] [CrossRef]
  3. Jahan, F.; Nanji, K.; Qidwai, W.; Qasim, R. Fibromyalgia syndrome: An overview of pathophysiology, diagnosis and management. Oman Med. J. 2012, 27, 192–195. [Google Scholar] [CrossRef] [PubMed]
  4. Arnold, L.M.; Bennett, R.M.; Crofford, L.J.; Dean, L.E.; Clauw, D.J.; Goldenberg, D.L.; Fitzcharles, M.A.; Paiva, E.S.; Staud, R.; Sarzi-Puttini, P.; et al. AAPT Diagnostic Criteria for Fibromyalgia. J. Pain. 2018. [Google Scholar] [CrossRef] [PubMed]
  5. Abbi, B.; Natelson, B.-H. Is chronic fatigue syndrome the same illness as fibromyalgia: Evaluating the ‘single syndrome’ hypothesis. QJM 2013, 106, 3–9. [Google Scholar] [CrossRef]
  6. Fukuda, K.; Straus, S.E.; Hickie, I.; Sharpe, M.C.; Dobbins, J.G.; Komaroff, A. The chronic fatigue syndrome: A comprehensive approach to its definition and study. International Chronic Fatigue Syndrome Study Group. Ann. Intern. Med. 1994, 121, 953–959. [Google Scholar] [CrossRef]
  7. Carruthers, B.M.; Jain, A.K.; De Meirleir, K.L.; Peterson, D.L.; Klimas, N.G.; Lerner, A.; Bested, A.C.; Flor-Henry, P.; Joshi, P.; Powles, A.C.P.; et al. Myalgic encephalomyelitis/chronic fatigue syndrome: Clinical working case definition, diagnostic and treatment protocols. J. Chronic Fatigue Syndr. 2003, 11, 7–115. [Google Scholar] [CrossRef]
  8. Carruthers, B.M.; van de Sande, M.I.; De Meirleir, K.L.; Klimas, N.G.; Broderick, G.; Mitchell, T.; Staines, D.; Powles, A.C.P.; Speight, N.; Vallings, R.; et al. Myalgic encephalomyelitis: International Consensus Criteria. J. Intern. Med. 2011, 270, 3273–3278. [Google Scholar] [CrossRef]
  9. Committee on the Diagnostic Criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome, Board on the Health of Select Populations, Institute of Medicine. Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness; National Academies Press (US): Washington, DC, USA, 2015. [Google Scholar] [PubMed]
  10. Clayton, E.W. Beyond myalgic encephalomyelitis/chronic fatigue syndrome: A IOM report on redefining an illness. JAMA 2015, 313, 1101–1102. [Google Scholar] [CrossRef] [PubMed]
  11. Jason, L.A.; McManimen, S.; Sunnquist, M.; Newton, J.L.; Strand, E.B. Clinical criteria versus a possible research case definition in chronic fatigue syndrome/myalgic encephalomyelitis. Fatigue 2017, 5, 89–102. [Google Scholar] [CrossRef]
  12. Boerma, T.; Harrison, J.; Jakob, R.; Mathers, C.; Schmider, A.; Weber, S. Revising the ICD: Explaining the WHO approach. Lancet 2016, 388, 2476–2477. [Google Scholar] [CrossRef]
  13. Jones, G.T.; Atzeni, F.; Beasley, M.; Flüß, E.; Sarzi-Puttini, P.; Macfarlane, G.J. The prevalence of fibromyalgia in the general population—A comparison of the American College of Rheumatology 1990, 2010 and modified 2010 classification criteria. Arthritis Rheumatol. 2014. [Google Scholar] [CrossRef]
  14. Cabo-Meseguer, A.; Cerdá-Olmedo, G.; Trillo-Mata, J.L. Fibromyalgia: Prevalence, epidemiologic profiles and economic costs. Med. Clin. (Barc) 2017, 149, 441–448. [Google Scholar] [CrossRef] [PubMed]
  15. Reyes, M.; Nisenbaum, R.; Hoaglin, D.C.; Unger, E.R.; Emmons, C.; Randall, B.; Stewart, J.A.; Abbey, S.; Jones, J.F.; Gantz, N.; et al. Prevalence and incidence of chronic fatigue syndrome in Wichita, Kansas. Arch. Intern. Med. 2003, 163, 1530–1536. [Google Scholar] [CrossRef]
  16. Jason, L.A.; Richman, J.A.; Rademaker, A.W.; Jordan, K.M.; Plioplys, A.V.; Taylor, R.R.; McCready, W.; Huang, C.-F.; Plioplys, S. A community-based study of chronic fatigue syndrome. Arch. Intern. Med. 1999, 159, 2129–2137. [Google Scholar] [CrossRef] [PubMed]
  17. Estévez-López, F.; Castro-Marrero, J.; Wang, X.; Bakken, I.J.; Ivanovs, A.; Nacul, L.; Sepúlveda, N.; Strand, E.B.; Pheby, D.; Alegre, J.; et al. European Network on ME/CFS (EUROMENE). Prevalence and incidence of myalgic encephalomyelitis/chronic fatigue syndrome in Europe-the Euro-epiME study from the European network EUROMENE: A protocol for a systematic review. BMJ Open. 2018, 8, e020817. [Google Scholar] [CrossRef]
  18. Crawley, E. Pediatric chronic fatigue syndrome: Current perspectives. Pediatr. Health Med. Ther. 2018, 9, 27–33. [Google Scholar] [CrossRef]
  19. McManimen, S.L.; Jason, L.A. Post-Exertional Malaise in Patients with ME and CFS with Comorbid Fibromyalgia. SRL Neurol. Neurosurg. 2017, 3, 22–27. [Google Scholar]
  20. Naschitz, J.E.; Rozenbaum, M.; Rosner, I.; Sabo, E.; Priselac, R.M.; Shaviv, N.; Ahdoot, A.; Ahdoot, M.; Gaitini, L.; Eldar, S.; et al. Cardiovascular response to upright tilt in fibromyalgia differs from that in chronic fatigue syndrome. J. Rheumatol. 2001, 28, 1356–1360. [Google Scholar]
  21. Naschitz, J.E.; Slobodin, G.; Sharif, D.; Fields, M.; Isseroff, H.; Sabo, E.; Rosner, I. Electrocardiographic QT interval and cardiovascular reactivity in fibromyalgia differ from chronic fatigue syndrome. Eur. J. Intern. Med. 2008, 19, 187–191. [Google Scholar] [CrossRef]
  22. Korszun, A.; Sackett-Lundeen, L.; Papadopoulos, E.; Brucksch, C.; Masterson, L.; Engelberg, N.C.; Haus, E.; Demitrack, M.A.; Crofford, L. Melatonin levels in women with fibromyalgia and chronic fatigue syndrome. J. Rheumatol. 1999, 26, 2675–2680. [Google Scholar] [PubMed]
  23. Crofford, L.J.; Young, E.A.; Engleberg, N.C.; Korszun, A.; Brucksch, C.B.; McClure, L.A.; Brown, M.B.; Demitrack, M.A. Basal circadian and pulsatile ACTH and cortisol secretion in patients with fibromyalgia and/or chronic fatigue syndrome. Brain Behav. Immun. 2004, 18, 314–325. [Google Scholar] [CrossRef] [PubMed]
  24. Light, A.R.; Bateman, L.; Jo, D.; Hughen, R.W.; Vanhaitsma, T.A.; White, A.T.; Light, K.C. Gene expression alterations at baseline and following moderate exercise in patients with Chronic Fatigue Syndrome and Fibromyalgia Syndrome. J. Intern. Med. 2012, 271, 64–81. [Google Scholar] [CrossRef] [PubMed]
  25. Scheibenbogen, C.; Freitag, H.; Blanco, J.; Capelli, E.; Lacerda, E.; Authier, J.; Meeus, M.; Marrero, J.C.; Nora-Krukle, Z.; Oltra, E.; et al. The European ME/CFS Biomarker Landscape project: An initiative of the European network EUROMENE. J. Transl. Med. 2017, 15, 162. [Google Scholar] [CrossRef] [PubMed]
  26. Xiao, C.; Rajewsky, K. MicroRNA control in the immune system: Basic principles. Cell. 2009, 137, 26–36, Erratum in: Cell 2009, 137, 380. [Google Scholar] [CrossRef] [PubMed]
  27. Emde, A.; Hornstein, E. miRNAs at the interface of cellular stress and disease. EMBO J. 2014, 33, 1428–1437. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Puik, J.R.; Meijer, L.L.; Le Large, T.Y.; Prado, M.M.; Frampton, A.E.; Kazemier, G.; Giovannetti, E. miRNA profiling for diagnosis, prognosis and stratification of cancer treatment in cholangiocarcinoma. Pharmacogenomics 2017, 18, 1343–1358. [Google Scholar] [CrossRef]
  29. Filipowicz, W.; Bhattacharyya, S.N.; Sonenberg, N. Mechanisms of post-transcriptional regulation by microRNAs: Are the answers in sight? Nat. Rev. Genet. 2008, 9, 102–114. [Google Scholar] [CrossRef]
  30. Vasudevan, S. Posttranscriptional upregulation by microRNAs. Wiley Interdiscip. Rev. RNA 2012, 3, 311–330. [Google Scholar] [CrossRef] [PubMed]
  31. Xia, T.; Li, J.; Cheng, H.; Zhang, C.; Zhang, Y. Small-Molecule Regulators of MicroRNAs in Biomedicine. Drug Dev. Res. 2015, 76, 375–381. [Google Scholar] [CrossRef]
  32. Cha, W.; Fan, R.; Miao, Y.; Zhou, Y.; Qin, C.; Shan, X.; Wan, X.; Cui, T. MicroRNAs as novel endogenous targets for regulation and therapeutic treatments. MedChemComm 2017, 9, 396–408. [Google Scholar] [CrossRef]
  33. Melo, S.; Villanueva, A.; Moutinho, C.; Davalos, V.; Spizzo, R.; Ivan, C.; Rossi, S.; Setien, F.; Casanovas, O.; Simo-Riudalbas, L.; et al. Small molecule enoxacin is a cancer-specific growth inhibitor that acts by enhancing TAR RNA-binding protein 2-mediated microRNA processing. Proc. Natl. Acad. Sci. USA 2011, 108, 4394–4399, Erratum in: Proc. Natl. Acad. Sci. USA 2017. [Google Scholar] [CrossRef] [Green Version]
  34. Watashi, K.; Yeung, M.L.; Starost, M.F.; Hosmane, R.S.; Jeang, K.T. Identification of small molecules that suppress microRNA function and reverse tumorigenesis. J. Biol. Chem. 2010, 285, 24707–24716. [Google Scholar] [CrossRef]
  35. Qiu, C.; Chen, G.; Cui, Q. Towards the understanding of microRNA and environmental factor interactions and their relationships to human diseases. Sci. Rep. 2012, 2, 318. [Google Scholar] [CrossRef]
  36. Yang, Q.; Qiu, C.; Yang, J.; Wu, Q.; Cui, Q. miREnvironment database: Providing a bridge for microRNAs, environmental factors and phenotypes. Bioinformatics 2011, 27, 3329–3330. [Google Scholar] [CrossRef]
  37. Luo, H.; Lan, W.; Chen, Q.; Wang, Z.; Liu, Z.; Yue, X.; Zhu, L. Inferring microRNA-environmental factor interactions based on multiple biological information fusion. Molecules 2018, 23, 2439. [Google Scholar] [CrossRef]
  38. Peng, C.; Wang, Y.L. Editorial: MicroRNAs as new players in endocrinology. Front Endocrinol. (Lausanne) 2018, 9, 459. [Google Scholar] [CrossRef]
  39. Smith-Vikos, T.; Slack, F.J. MicroRNAs and their roles in aging. J. Cell Sci. 2012, 125 Pt 1, 7–17. [Google Scholar] [CrossRef] [Green Version]
  40. Rani, A.; O’Shea, A.; Ianov, L.; Cohen, R.A.; Woods, A.J.; Foster, T.C. miRNA in Circulating Microvesicles as Biomarkers for Age-Related Cognitive Decline. Front Aging Neurosci. 2017, 9, 323. [Google Scholar] [CrossRef]
  41. Vienberg, S.; Geiger, J.; Madsen, S.; Dalgaard, L.T. MicroRNAs in metabolism. Acta Physiol. (Oxf.) 2017, 219, 346–361. [Google Scholar] [CrossRef]
  42. Lopez, S.; Bermudez, B.; Montserrat-de la Paz, S.; Abia, R.; Muriana, F.J.G. A microRNA expression signature of the postprandial state in response to a high-saturated-fat challenge. J. Nutr. Biochem. 2018, 57, 45–55. [Google Scholar] [CrossRef] [PubMed]
  43. Sapp, R.M.; Shill, D.D.; Roth, S.M.; Hagberg, J.M. Circulating microRNAs in acute and chronic exercise: More than mere biomarkers. J. Appl. Physiol. 2017, 122, 702–717. [Google Scholar] [CrossRef] [PubMed]
  44. Häuser, W.; Welsch, P.; Klose, P.; Derry, S.; Straube, S.; Wiffen, P.J.; Moore, R. Pharmacological therapies for fibromyalgia in adults—An overview of Cochrane Reviews. Cochrane Database Syst. Rev. 2018. [Google Scholar] [CrossRef]
  45. Thorpe, J.; Shum, B.; Moore, R.A.; Wiffen, P.J.; Gilron, I. Combination pharmacotherapy for the treatment of fibromyalgia in adults. Cochrane Database Syst. Rev. 2018. [Google Scholar] [CrossRef]
  46. Collatz, A.; Johnston, S.C.; Staines, D.R.; Marshall-Gradisnik, S.M. A Systematic Review of Drug Therapies for Chronic Fatigue Syndrome/Myalgic Encephalomyelitis. Clin. Ther. 2016, 38, 1263–1271. [Google Scholar] [CrossRef]
  47. Smith, M.E.; Haney, E.; McDonagh, M.; Pappas, M.; Daeges, M.; Wasson, N.; Fu, R.; Nelson, H.D. Treatment of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Systematic Review for a National Institutes of Health Pathways to Prevention Workshop. Ann. Intern. Med. 2015, 162, 841–850. [Google Scholar] [CrossRef] [PubMed]
  48. Castro-Marrero, J.; Sáez-Francàs, N.; Santillo, D.; Alegre, J. Treatment and management of chronic fatigue syndrome/myalgic encephalomyelitis: All roads lead to Rome. Br. J. Pharmacol. 2017, 174, 345–369. [Google Scholar] [CrossRef]
  49. NINDS. Common Data Elements. Available online: https://www.commondataelements.ninds.nih.gov/MECFS.aspx#tab=Data_Standards (accessed on 15 November 2018).
  50. Rukov, J.L.; Wilentzik, R.; Jaffe, I.; Vinther, J.; Shomron, N. Pharmaco-miR: Linking microRNAs and drug effects. Brief Bioinform. 2014, 15, 648–659. [Google Scholar] [CrossRef]
  51. Russo, F.; Di Bella, S.; Bonnici, V.; Laganà, A.; Rainaldi, G.; Pellegrini, M.; Pulvirenti, A.; Giugno, R.; Ferro, A.; et al. A knowledge base for the discovery of function, diagnostic potential and drug effects on cellular and extracellular miRNAs. BMC Genom. 2014, 15 (Suppl. 3), S4. [Google Scholar] [CrossRef] [PubMed]
  52. Zhang, G.; Nebert, D.W. Personalized medicine: Genetic risk prediction of drug response. Pharmacol Ther. 2017, 175, 75–90. [Google Scholar] [CrossRef]
  53. Lloyd, R.A.; Hotham, E.; Hall, C.; Williams, M.; Suppiah, V. Pharmacogenomics and patient treatment parameters to opioid treatment in chronic pain: A focus on Morphine, Oxycodone, Tramadol, and Fentanyl. Pain Med. 2017, 18, 2369–2387. [Google Scholar] [CrossRef] [PubMed]
  54. Rukov, J.L.; Shomron, N. MicroRNA pharmacogenomics: Post-transcriptional regulation of drug response. Trends Mol. Med. 2011, 17, 412–423. [Google Scholar] [CrossRef] [PubMed]
  55. Bracco, L.; Kearsey, J. The relevance of alternative RNA splicing to pharmacogenomics. Trends Biotechnol. 2003, 21, 346–353. [Google Scholar] [CrossRef]
  56. Ivanov, M.; Kacevska, M.; Ingelman-Sundberg, M. Epigenomics and interindividual differences in drug response. Clin. Pharmacol. Ther. 2012, 92, 727–736. [Google Scholar] [CrossRef] [PubMed]
  57. Hu, H.; Baines, C. Recent insights into 3 underrecognized conditions: Myalgic encephalomyelitis-chronic fatigue syndrome, fibromyalgia, and environmental sensitivities-multiple chemical sensitivity. Can. Fam. Physician 2018, 64, 413–415. [Google Scholar]
  58. Liberati, A.; Altman, D.G.; Tetzlaff, J.; Mulrow, C.; Gøtzsche, P.C.; Ioannidis, J.P.; Clarke, M.; Devereaux, P.J.; Kleijnen, J.; Moher, D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. PLoS Med. 2009, 6, e1000100. [Google Scholar] [CrossRef]
  59. PubMed US National Library of Medicine National Institutes of Health. Available online: https://www.ncbi.nlm.nih.gov/pubmed (accessed on 10 October 2018).
  60. FECYT. Fundación Española para la Ciencia y la Tecnología. Web of Science (WOS). Available online: https://www.fecyt.es/es/recurso/web-science (accessed on 10 October 2018).
  61. Pubmed Labs. Bethesda (MD): National Library of Medicine (US), National Center for Biotechnology Information. 2018. Available online: https://www.ncbi.nlm.nih.gov/labs/pubmed/ (accessed on 10 October 2018).
  62. Cochrane Library. Available online: https://www.cochranelibrary.com (accessed on 11 October 2018).
  63. Kozomara, A.; Griffiths-Jones, S. miRBase: Integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2011, 39, D152–D157. [Google Scholar] [CrossRef]
  64. Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 2018, 46, D1074–D1082. [Google Scholar] [CrossRef]
  65. Liu, X.; Wang, S.; Meng, F.; Wang, J.; Zhang, Y.; Dai, E.; Yu, X.; Li, X.; Jiang, W. SM2miR: A database of the experimentally validated small molecules’ effects on microRNA expression. Bioinformatics 2013, 29, 409–411. [Google Scholar] [CrossRef]
  66. Brown, A.S.; Patel, C.J. A standard database for drug repositioning. Sci Data 2017, 4, 170029. [Google Scholar] [CrossRef] [Green Version]
  67. Bjersing, J.L.; Lundborg, C.; Bokarewa, M.I.; Mannerkorpi, K. Profile of cerebrospinal microRNAs in fibromyalgia. PLoS ONE 2013, 8, e78762. [Google Scholar] [CrossRef]
  68. Bjersing, J.L.; Bokarewa, M.I.; Mannerkorpi, K. Profile of circulating microRNAs in fibromyalgia and their relation to symptom severity: An exploratory study. Rheumatol. Int. 2015, 35, 635–642. [Google Scholar] [CrossRef]
  69. Cerdá-Olmedo, G.; Mena-Durán, A.V.; Monsalve, V.; Oltra, E. Identification of a microRNA signature for the diagnosis of fibromyalgia. PLoS ONE 2015, 10, e0121903. [Google Scholar] [CrossRef]
  70. Masotti, A.; Baldassarre, A.; Guzzo, M.P.; Iannuccelli, C.; Barbato, C.; Di Franco, M. Circulating microRNA Profiles as Liquid Biopsies for the Characterization and Diagnosis of Fibromyalgia Syndrome. Mol. Neurobiol. 2017, 54, 7129–7136. [Google Scholar] [CrossRef]
  71. Leinders, M.; Doppler, K.; Klein, T.; Deckart, M.; Rittner, H.; Sommer, C.; Üçeyler, N. Increased cutaneous miR-let-7d expression correlates with small nerve fiber pathology in patients with fibromyalgia syndrome. Pain 2016, 157, 2493–2503. [Google Scholar] [CrossRef]
  72. Brenu, E.W.; Ashton, K.J.; van Driel, M.; Staines, D.R.; Peterson, D.; Atkinson, G.M.; Marshall-Gradisnik, S.M. Cytotoxic lymphocyte microRNAs as prospective biomarkers for Chronic Fatigue Syndrome/Myalgic Encephalomyelitis. J. Affect Disord. 2012, 141, 261–269. [Google Scholar] [CrossRef]
  73. Brenu, E.W.; Ashton, K.J.; Batovska, J.; Staines, D.R.; Marshall-Gradisnik, S.M. High-throughput sequencing of plasma microRNA in chronic fatigue syndrome/myalgic encephalomyelitis. PLoS ONE 2014, 9, e102783. [Google Scholar] [CrossRef]
  74. Petty, R.D.; McCarthy, N.E.; Le Dieu, R.; Kerr, J.R. MicroRNAs hsa-miR-99b, hsa-miR-330, hsa-miR-126 and hsa-miR-30c: Potential diagnostic biomarkers in Natural Killer (NK) cells of patients with Chronic Fatigue Syndrome (CFS)/Myalgic Encephalomyelitis (ME). PLoS ONE 2016, 11, e0150904. [Google Scholar] [CrossRef]
  75. Baraniuk, J.N.; Shivapurkar, N. Exercise-induced changes in cerebrospinal fluid miRNAs in Gulf War Illness, Chronic Fatigue Syndrome and sedentary control subjects. Sci. Rep. 2017, 7, 15338, Erratum in: Sci. Rep. 2018, 8, 6455. [Google Scholar] [CrossRef]
  76. Cording, M.; Derry, S.; Phillips, T.; Moore, R.A.; Wiffen, P.J. Milnacipran for pain in fibromyalgia in adults. Cochrane Database Syst. Rev. 2015, CD008244. [Google Scholar] [CrossRef]
  77. Lunn, M.P.; Hughes, R.A.; Wiffen, P.J. Duloxetine for treating painful neuropathy, chronic pain or fibromyalgia. Cochrane Database Syst. Rev. 2014, CD007115. [Google Scholar] [CrossRef] [PubMed]
  78. Walitt, B.; Urrútia, G.; Nishishinya, M.B.; Cantrell, S.E.; Häuser, W. Selective serotonin reuptake inhibitors for fibromyalgia syndrome. Cochrane Database Syst. Rev. 2015, CD011735. [Google Scholar] [CrossRef]
  79. Riera, R. Selective serotonin reuptake inhibitors for fibromyalgia syndrome. Sao Paulo Med. J. 2015, 133, 454. [Google Scholar] [CrossRef] [PubMed]
  80. Moore, R.A.; Derry, S.; Aldington, D.; Cole, P.; Wiffen, P.J. Amitriptyline for neuropathic pain in adults. Cochrane Database Syst. Rev. 2015, CD008242. [Google Scholar] [CrossRef] [PubMed]
  81. Tort, S.; Urrútia, G.; Nishishinya, M.B.; Walitt, B. Monoamine oxidase inhibidors (MAOIs) for fibromyalgia syndrome. Cochrane Database Syst. Rev. 2012, CD009807. [Google Scholar] [CrossRef]
  82. Welsch, P.; Bernardy, K.; Derry, S.; Moore, R.A.; Häuser, W. Mirtazapine for fibromyalgia in adults. Cochrane Database Syst. Rev. 2018, CD012708. [Google Scholar] [CrossRef]
  83. Birse, F.; Derry, S.; Moore, R.A. Phenytoin for neuropathic pain and fibromyalgia in adults. Cochrane Database Syst. Rev. 2012, CD009485. [Google Scholar] [CrossRef]
  84. Gill, D.; Derry, S.; Wiffen, P.J.; Moore, R.A. Valproic acid and sodium valproate for neuropathic pain and fibromyalgia in adults. Cochrane Database Syst. Rev. 2011, CD009183. [Google Scholar] [CrossRef]
  85. Corrigan, R.; Derry, S.; Wiffen, P.J.; Moore, R.A. Clonazepam for neuropathic pain and fibromyalgia in adults. Cochrane Database Syst. Rev. 2012, CD009486. [Google Scholar] [CrossRef] [PubMed]
  86. Derry, S.; Cording, M.; Wiffen, P.J.; Law, S.; Phillips, T.; Moore, R.A. Pregabalin for pain in fibromyalgia in adults. Cochrane Database Syst. Rev. 2016, CD011790. [Google Scholar] [CrossRef]
  87. Wiffen, P.J.; Derry, S.; Bell, R.F.; Rice, A.S.; Tölle, T.R.; Phillips, T.; Moore, R.A. Gabapentin for chronic neuropathic pain in adults. Cochrane Database Syst. Rev. 2017, CD007938. [Google Scholar] [CrossRef]
  88. Hearn, L.; Derry, S.; Moore, R.A. Lacosamide for neuropathic pain and fibromyalgia in adults. Cochrane Database Syst. Rev. 2012, CD009318. [Google Scholar] [CrossRef] [PubMed]
  89. Wiffen, P.J.; Derry, S.; Lunn, M.P.; Moore, R.A. Topiramate for neuropathic pain and fibromyalgia in adults. Cochrane Database Syst. Rev. 2013, CD008314. [Google Scholar] [CrossRef]
  90. Walitt, B.; Klose, P.; Üçeyler, N.; Phillips, T.; Häuser, W. Antipsychotics for fibromyalgia in adults. Cochrane Database Syst. Rev. 2016, CD011804. [Google Scholar] [CrossRef] [PubMed]
  91. Walitt, B.; Klose, P.; Fitzcharles, M.A.; Phillips, T.; Häuser, W. Cannabinoids for fibromyalgia. Cochrane Database Syst. Rev. 2016, CD011694. [Google Scholar] [CrossRef]
  92. Derry, S.; Wiffen, P.J.; Häuser, W.; Mücke, M.; Tölle, T.R.; Bell, R.F.; Moore, R.A. Oral nonsteroidal anti-inflammatory drugs for fibromyalgia in adults. Cochrane Database Syst. Rev. 2017, CD012332. [Google Scholar] [CrossRef] [PubMed]
  93. Gaskell, H.; Moore, R.A.; Derry, S.; Stannard, C. Oxycodone for pain in fibromyalgia in adults. Cochrane Database Syst. Rev. 2016, CD012329. [Google Scholar] [CrossRef] [PubMed]
  94. Larun, L.; Brurberg, K.G.; Odgaard-Jensen, J.; Price, J.R. Exercise therapy for chronic fatigue syndrome. Cochrane Database Syst. Rev. 2017, CD003200. [Google Scholar] [CrossRef]
  95. Price, J.R.; Mitchell, E.; Tidy, E.; Hunot, V. Cognitive behaviour therapy for chronic fatigue syndrome in adults. Cochrane Database Syst. Rev. 2008, CD001027. [Google Scholar] [CrossRef]
  96. Adams, D.; Wu, T.; Yang, X.; Tai, S.; Vohra, S. Traditional Chinese medicinal herbs for the treatment of idiopathic chronic fatigue and chronic fatigue syndrome. Cochrane Database Syst. Rev. 2009, CD006348. [Google Scholar] [CrossRef]
  97. Blockmans, D.; Persoons, P. Long-term methylphenidate intake in chronic fatigue syndrome. Acta Clin. Belg. 2016, 71, 407–414. [Google Scholar] [CrossRef]
  98. Nilsson, M.K.L.; Zachrisson, O.; Gottfries, C.G.; Matousek, M.; Peilot, B.; Forsmark, S.; Schuit, R.C.; Carlsson, M.L.; Kloberg, A.; Carlsson, A. A randomised controlled trial of the monoaminergic stabiliser (-)-OSU6162 in treatment of myalgic encephalomyelitis/chronic fatigue syndrome. Acta Neuropsychiatr. 2018, 30, 148–157. [Google Scholar] [CrossRef]
  99. Proal, A.D.; Albert, P.J.; Marshall, T.G.; Blaney, G.P.; Lindseth, I.A. Immunostimulation in the treatment for chronic fatigue syndrome/myalgic encephalomyelitis. Immunol. Res. 2013, 56, 398–412. [Google Scholar] [CrossRef]
  100. Plioplys, A.V.; Plioplys, S. Amantadine and L-carnitine treatment of Chronic Fatigue Syndrome. Neuropsychobiology 1997, 35, 16–23. [Google Scholar] [CrossRef]
  101. Castro-Marrero, J.; Cordero, M.D.; Segundo, M.J.; Sáez-Francàs, N.; Calvo, N.; Román-Malo, L.; Aliste, L.; Fernández de Sevilla, T.; Alegre, J. Does oral coenzyme Q10 plus NADH supplementation improve fatigue and biochemical parameters in chronic fatigue syndrome? Antioxid. Redox Signal. 2015, 22, 679–685. [Google Scholar] [CrossRef]
  102. Blockmans, D.; Persoons, P.; Van Houdenhove, B.; Lejeune, M.; Bobbaers, H. Combination therapy with hydrocortisone and fludrocortisone does not improve symptoms in chronic fatigue syndrome: A randomized, placebo-controlled, double-blind, crossover study. Am. J. Med. 2003, 114, 736–741. [Google Scholar] [CrossRef]
  103. Strayer, D.R.; Carter, W.A.; Stouch, B.C.; Stevens, S.R.; Bateman, L.; Cimoch, P.J.; Lapp, C.W.; Peterson, D.L. Chronic Fatigue Syndrome AMP-516 Study Group; Mitchell WM. A double-blind, placebo-controlled, randomized, clinical trial of the TLR-3 agonist rintatolimod in severe cases of chronic fatigue syndrome. PLoS ONE 2012, 7, e31334. [Google Scholar] [CrossRef]
  104. Comhaire, F. Treating patients suffering from myalgic encephalopathy/chronic fatigue syndrome (ME/CFS) with sodium dichloroacetate: An open-label, proof-of-principle pilot trial. Med. Hypotheses. 2018, 114, 454–458. [Google Scholar] [CrossRef]
  105. Ostojic, S.M.; Stojanovic, M.; Drid, P.; Hoffman, J.R.; Sekulic, D.; Zenic, N. Supplementation with Guanidinoacetic Acid in Women with Chronic Fatigue Syndrome. Nutrients 2016, 8, 72. [Google Scholar] [CrossRef]
  106. Gil-Zamorano, J.; Martin, R.; Daimiel, L.; Richardson, K.; Giordano, E.; Nicod, N.; García-Carrasco, B.; Soares, S.M.; Iglesias-Gutiérrez, E.; Lasunción, M.A.; et al. Docosahexaenoic acid modulates the enterocyte Caco-2 cell expression of microRNAs involved in lipid metabolism. J. Nutr. 2014, 144, 575–585. [Google Scholar] [CrossRef] [PubMed]
  107. Rodrigues, A.C.; Li, X.; Radecki, L.; Pan, Y.Z.; Winter, J.C.; Huang, M.; Yu, A.M. MicroRNA expression is differentially altered by xenobiotic drugs in different human cell lines. Biopharm. Drug. Dispos. 2011, 32, 355–367. [Google Scholar] [CrossRef] [Green Version]
  108. Rainer, J.; Ploner, C.; Jesacher, S.; Ploner, A.; Eduardoff, M.; Mansha, M.; Wasim MPanzer-Grümayer, R.; Trajanoski, Z.; Niederegger, H.; et al. Glucocorticoid-regulated microRNAs and mirtrons in acute lymphoblastic leukemia. Leukemia 2009, 23, 746–752. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  109. Lu, S.; Mukkada, V.A.; Mangray, S.; Cleveland, K.; Shillingford, N.; Schorl, C.; Brodsky, A.S.; Resnick, M.B. MicroRNA profiling in mucosal biopsies of eosinophilic esophagitis patients pre and post treatment with steroids and relationship with mRNA targets. PLoS ONE 2012, 7, e40676. [Google Scholar] [CrossRef] [PubMed]
  110. Dave, R.S.; Khalili, K. Morphine treatment of human monocyte-derived macrophages induces differential miRNA and protein expression: Impact on inflammation and oxidative stress in the central nervous system. J. Cell Biochem. 2010, 110, 834–845. [Google Scholar] [CrossRef]
  111. Fayyad-Kazan, H.; Rouas, R.; Merimi, M.; El Zein, N.; Lewalle, P.; Jebbawi, F.; Mourtada, M.; Badran, H.; Ezzeddine, M.; Salaun, B.; et al. Valproate treatment of human cord blood CD4-positive effector T cells confers on them the molecular profile (microRNA signature and FOXP3 expression) of natural regulatory CD4-positive cells through inhibition of histone deacetylase. J. Biol. Chem. 2010, 285, 2048–2191. [Google Scholar] [CrossRef]
  112. Lukkahatai, N.; Walitt, B.; Deandrés-Galiana, E.J.; Fernández-Martínez, J.L.; Saligan, L.N. A predictive algorithm to identify genes that discriminate individuals with fibromyalgia syndrome diagnosis from healthy controls. J. Pain Res. 2018, 11, 2981–2990. [Google Scholar] [CrossRef]
  113. Ameling, S.; Kacprowski, T.; Chilukoti, R.K.; Malsch, C.; Liebscher, V.; Suhre, K.; Pietzner, M.; Friedrich, N.; Homuth, G.; Hammer, E.; et al. Associations of circulating plasma microRNAs with age, body mass index and sex in a population-based study. BMC Med. Genom. 2015, 8, 61. [Google Scholar] [CrossRef]
  114. Peng, X.; Li, W.; Yuan, L.; Mehta, R.G.; Kopelovich, L.; McCormick, D.L. Inhibition of proliferation and induction of autophagy by atorvastatin in PC3 prostate cancer cells correlate with downregulation of Bcl2 and upregulation of miR-182 and p21. PLoS ONE 2013, 8, e70442. [Google Scholar] [CrossRef]
  115. Minami, Y.; Satoh, M.; Maesawa, C.; Takahashi, Y.; Tabuchi, T.; Itoh, T.; Nakamura, M. Effect of atorvastatin on microRNA 221/222 expression in endothelial progenitor cells obtained from patients with coronary artery disease. Eur. J. Clin. Investig. 2009, 39, 359–367. [Google Scholar] [CrossRef]
  116. Saito, Y.; Suzuki, H.; Imaeda, H.; Matsuzaki, J.; Hirata, K.; Tsugawa, H.; Hibino, S.; Kanai, Y.; Saito, H.; Hibi, T. The tumor suppressor microRNA-29c is downregulated and restored by celecoxib in human gastric cancer cells. Int. J. Cancer 2013, 132, 1751–1760. [Google Scholar] [CrossRef]
  117. Li, Y.; Kong, D.; Ahmad, A.; Bao, B.; Dyson, G.; Sarkar, F.H. Epigenetic deregulation of miR-29a and miR-1256 by isoflavone contributes to the inhibition of prostate cancer cell growth and invasion. Epigenetics 2012, 7, 940–949. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  118. Li, Y.; Kong, D.; Ahmad, A.; Bao, B.; Sarkar, F.H. Targeting bone remodeling by isoflavone and 3,3′-diindolylmethane in the context of prostate cancer bone metastasis. PLoS ONE 2012, 7, e33011. [Google Scholar] [CrossRef] [PubMed]
  119. Bao, B.; Wang, Z.; Ali, S.; Ahmad, A.; Azmi, A.S.; Sarkar, S.H.; Banerjee, S.; Kong, D.; Li, Y.; Thaku, S.; et al. Metformin inhibits cell proliferation, migration and invasion by attenuating CSC function mediated by deregulating miRNAs in pancreatic cancer cells. Cancer Prev. Res. (Phila) 2012, 5, 355–364. [Google Scholar] [CrossRef]
  120. Chandrasekar, V.; Dreyer, J.L. microRNAs miR-124, let-7d and miR-181a regulate cocaine-induced plasticity. Mol. Cell Neurosci. 2009, 42, 350–362. [Google Scholar] [CrossRef]
  121. Mantri, C.K.; Mantri, J.V.; Pandhare, J.; Dash, C. Methamphetamine inhibits HIV-1 replication in CD4+ T cells by modulating anti-HIV-1 miRNA expression. Am. J. Pathol. 2014, 184, 92–100. [Google Scholar] [CrossRef]
  122. Guo, Y.; Chen, Y.; Carreon, S.; Qiang, M. Chronic intermittent ethanol exposure and its removal induce a different miRNA expression pattern in primary cortical neuronal cultures. Alcohol Clin. Exp. Res. 2012, 36, 1058–1066. [Google Scholar] [CrossRef]
  123. Shin, V.Y.; Jin, H.; Ng, E.K.; Cheng, A.S.; Chong, W.W.; Wong, C.Y.; Leung, W.K.; Sung, J.J.Y.; Chu, K.-M. NF-κB targets miR-16 and miR-21 in gastric cancer: Involvement of prostaglandin E receptors. Carcinogenesis 2011, 32, 240–245. [Google Scholar] [CrossRef] [PubMed]
  124. Zhang, Y.; Pan, T.; Zhong, X.; Cheng, C. Nicotine upregulates microRNA-21 and promotes TGF-β-dependent epithelial-mesenchymal transition of esophageal càncer cells. Tumour Biol. 2014, 35, 7063–7072. [Google Scholar] [CrossRef]
  125. Maccani, M.A.; Avissar-Whiting, M.; Banister, C.E.; McGonnigal, B.; Padbury, J.F.; Marsit, C.J. Maternal cigarette smoking during pregnancy is associated with downregulation of miR-16, miR-21, and miR-146a in the placenta. Epigenetics 2010, 5, 583–589. [Google Scholar] [CrossRef]
  126. Bhat-Nakshatri, P.; Wang, G.; Collins, N.R.; Thomson, M.J.; Geistlinger, T.R.; Carroll, J.S.; Brown, M.; Hammond, S.; Srour, E.F.; Liu, Y.; et al. Estradiol-regulated microRNAs control estradiol response in breast cancer cells. Nucleic Acids Res. 2009, 37, 4850–4861. [Google Scholar] [CrossRef] [Green Version]
  127. Tilghman, S.L.; Bratton, M.R.; Segar, H.C.; Martin, E.C.; Rhodes, L.V.; Li, M.; McLachlan, J.A.; Wiese, T.E.; Nephew, K.P.; Burow, M.E. Endocrine disruptor regulation of microRNA expression in breast carcinoma cells. PLoS ONE 2012, 7, e32754. [Google Scholar] [CrossRef]
  128. Wickramasinghe, N.S.; Manavalan, T.T.; Dougherty, S.M.; Riggs, K.A.; Li, Y.; Klinge, C.M. Estradiol downregulates miR-21 expression and increases miR-21 target gene expression in MCF-7 breast cancer cells. Nucleic Acids Res. 2009, 37, 2584–2595. [Google Scholar] [CrossRef] [Green Version]
  129. Waltering, K.K.; Porkka, K.P.; Jalava, S.E.; Urbanucci, A.; Kohonen, P.J.; Latonen, L.M.; Kallioniemi, O.P.; Jenster, G.; Visakorpi, T. Androgen regulation of micro-RNAs in prostate cancer. Prostate 2011, 71, 604–614. [Google Scholar] [CrossRef] [PubMed]
  130. Yu, X.; Zhang, X.; Dhakal, I.B.; Beggs, M.; Kadlubar, S.; Luo, D. Induction of cell proliferation and survival genes by estradiol-repressed microRNAs in breast cancer cells. BMC Cancer 2012, 12, 29. [Google Scholar] [CrossRef] [PubMed]
  131. Feng, B.; Cao, Y.; Chen, S.; Ruiz, M.; Chakrabarti, S. miRNA-1 regulates endothelin-1 in diabetes. Life Sci. 2014, 98, 18–23. [Google Scholar] [CrossRef] [PubMed]
  132. Du, B.; Ma, L.M.; Huang, M.B.; Zhou, H.; Huang, H.L.; Shao, P.; Chen, Y.Q.; Qu, L.H. High glucose down-regulates miR-29a to increase collagen IV production in HK-2 cells. FEBS Lett. 2010, 584, 811–816. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  133. Bae, J.; Won, M.; Kim, D.Y.; Kim, J.H.; Kim, Y.M.; Kim, Y.T.; Nam, J.H.; Suh, D.S. Identification of differentially expressed microRNAs in endometrial cancer cells after progesterone treatment. Int. J. Gynecol. Cancer 2012, 22, 561–565. [Google Scholar] [CrossRef] [PubMed]
  134. Cochrane, D.R.; Jacobsen, B.M.; Connaghan, K.D.; Howe, E.N.; Bain, D.L.; Richer, J.K. Progestin regulated miRNAs that mediate progesterone receptor action in breast cancer. Mol. Cell Endocrinol. 2012, 355, 15–24. [Google Scholar] [CrossRef] [Green Version]
  135. Wang, W.L.; Chatterjee, N.; Chittur, S.V.; Welsh, J.; Tenniswood, M.P. Effects of 1α,25 dihydroxyvitamin D3 and testosterone on miRNA and mRNA expression in LNCaP cells. Mol. Cancer 2011, 10, 58. [Google Scholar] [CrossRef] [PubMed]
  136. Jorde, R.; Svartberg, J.; Joakimsen, R.M.; Coucheron, D.H. Plasma profile of microRNA after supplementation with high doses of vitamin D3 for 12 months. BMC Res. Notes 2012, 5, 245. [Google Scholar] [CrossRef] [PubMed]
  137. Chang, C.M.; Warren, J.L.; Engels, E.A. Chronic fatigue syndrome and subsequent risk of cancer among elderly US adults. Cancer 2012, 118, 5929–5936. [Google Scholar] [CrossRef] [PubMed]
  138. Daniels, S.I.; Sillé, F.C.; Goldbaum, A.; Yee, B.; Key, E.F.; Zhang, L.; Smith, M.T.; Thomas, R. Improving power to detect changes in blood miRNA expression by accounting for sources of variability in experimental designs. Cancer Epidemiol. Biomarkers Prev. 2014, 23, 2658–2666. [Google Scholar] [CrossRef] [PubMed]
  139. Foye, C.; Yan, I.K.; David, W.; Shukla, N.; Habboush, Y.; Chase, L.; Smith, M.T.; Thomas, R. Comparison of miRNA quantitation by Nanostring in serum and plasma samples. PLoS ONE 2017, 12, e0189165. [Google Scholar] [CrossRef] [PubMed]
  140. Ludwig, N.; Leidinger, P.; Becker, K.; Backes, C.; Fehlmann, T.; Pallasch, C.; Rheinheimer, S.; Meder, B.; Stähler, C.; Meese, E.; et al. Distribution of miRNA expression across human tissues. Nucleic Acids Res. 2016, 44, 3865–3877. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  141. Awan, H.M.; Shah, A.; Rashid, F.; Shan, G. Primate-specific Long Non-coding RNAs and MicroRNAs. Genom. Proteom. Bioinform. 2017, 15, 1871–1895. [Google Scholar] [CrossRef]
Table
Table 1. Summary of studies evaluating fibromyalgia (FM) miRNomes by multiplex approaches.
Table 1. Summary of studies evaluating fibromyalgia (FM) miRNomes by multiplex approaches.
Source of RNADiagnostic Criteria /Clinical ParametersCohortsTechnical ApproachOver-Expressed microRNAsUnder-Expressed microRNAsRT-qPCR Validated miRNAsGO Terms Mainly AffectedReferences
Cerebrospinal fluid (CSF)ACR 1990, FIQ & MFI-20 *10 FM
8 HC		microRNA Ready-to-Use PCR microchip (Exiqon, Denmark Cat No 203608) miR-21-5p, miR-145-5p, miR-29a-3p, miR-99b-5p, miR-125b-5p, miR-23a-3p, miR-23b-3p, miR-195-5p, miR-223-3pN/AGlial and neuronal response, insulin-like growth factor pathway, Alzheimer’s and Parkinson’s, autoimmunity and energy metabolismBjersing et al., 2013 [67]
SerumACR 1990, FIQ & MFI-20 *20 FM
20 HC		microRNA Ready-to-Use PCR microchip
(Exiqon, Denmark Cat No 203608)		miR-320amiR-103a-3p, miR-107, let-7a-5p, mir-30b-5p, miR-151a-5p, miR-142-3p, miR-374b-5p.N/ANeuronal regeneration, opioid tolerance, dopamine neurotransmitter receptor activity, cell division, stress response, energy metabolism, lipid metabolism, Alzheimer’sBjersing et al., 2014 [68]
PBMCsACR 1990, FIQ & MFI-20 *11 FM
10 HC		3D-Gene Human miRNA Oligo chips (version 16.0; Toray Industries) miR-223-3p, miR-451a, miR-338-3p, miR-143-3p, miR-145-5p, miR-21-5pmiR-223-3p, miR-451a, miR-338-3p, miR-143-3p, miR-145-5pNervous system, inflammation, diabetes, major depressive disorderCerdá-Olmedo et al., 2015 [69]
SerumACR 1990/2010, FIQ, FAS, HAQ & ZSAS/ZSDS *14 FM
14 HC		Serum/Plasma Focus miRNA PCR Panel I+II (96-wells Exiqon)Pooled Sera: miR-10a-5p, miR-320b, miR-424-5pPooled Sera: miR-20a-3p, miR-139-5p
Individual Sera: miR-23a-3p, miR-1, miR-133a, miR-346, miR-139-5p, miR-320b		N/ABrain development, immune response, osteogenesis, myoblast differentiation, autism, epilepsy, cellular proliferation and differentiation, muscular atrophy, complex regional pain syndrome, among othersMasotti et al., 2016 [70]
White blood cell
(WBC)		ACR 1990, FIQ, NPSI-G,
GCPS & ADS *		30 FM
34 HC		miRCURY LNA miRNA array (Exiqon, Vedbaek, version 19.0, with 2042 analyzed microRNAs)miR-136-5p, miR-4306, miR-744-5p, miR-4301, miR-151a-3p, miR-584-5p, miR-4288, miR-221-3p, miR-151a-5p, miR-199a-5p, miR-126-3p, miR-126-5p, miR-130a-3p, miR-146a-5p, miR-125a-5p, miR-4429, miR-320b, miR-320a, miR-320c, miR-17-3p, miR-423-3p, miR-425-5p, miR-4291, miR-652-3p, miR-103b-3p, miR-199a-3p, miR-335-5p, miR-331-3p, miR-339-5p, miR-92a-3p, let-7b-5p, miR-222-3p, miR-33a, let-7i-5p, miR-185-5p, miR-22-3p, miR-148b-3p, miR-103a-3p, let-7d-5p, miR-4289, miR-107, miR-30d-5p, miR-301a-3p, miR-374c-5p, miR-17-5p, miR-18b-5p, miR-1miR-4639-3p, miR-3685, miR-943, miR-877-3pmiR-199a, miR-151, miR-103, Let-7d, miR-146aCell proliferation, differentiation, brain development, opioid toleranceLeinders et al., 2016 [71]
* ACR: American College of Rheumatology 1990/2010 criteria; FIQ; Fibromyalgia Impact Questionnaire; MFI-20: Multidimensional Fatigue Inventory; FAS: Fibromyalgia Assessment Status; HAQ: Health Assessment Questionnaire; ZSAS, ZSDS: Zung Self-Rating Anxiety and Zung Self-Rating Depression Scale; NPSI-G: Neuropathic Pain Symptom Inventory; GCPS: Graded Chronic Pain Scale; ADS: Allgemeine Depressions-Skala. Bolded miRs correspond to miRs differentially expressed (DE) according to more than one FM study. Underlined miRs correspond to miRs DE in FM and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) studies.
Table
Table 2. Summary of studies evaluating ME/CFS miRNomes by multiplex approaches.
Table 2. Summary of studies evaluating ME/CFS miRNomes by multiplex approaches.
Source of RNADiagnostic CriteriaCohortsTechnical ApproachOver-Expressed microRNAsUnder-Expressed microRNAsRT-qPCR Validated microRNAsGO Terms Mainly AffectedReferences
NK & CD8+ cellsFukuda28 ME/CFS
28 HC		Analyzed by RT-qPCR 19 microRNAs: miR-10a miR-16, miR-15b, miR-107, miR-128b, miR-146a, miR-191, miR-21, miR-223, miR-17-5p, miR-150, miR-103, miR-106b, miR-126, miR-142-3p, miR-146-5p, miR-152, miR-181, let-7a. NK: miR-10a, miR-146a, miR-191, miR-223, miR-17-5p, miR-21, miR-106, miR-152, miR-103
CD8+: miR-21		N/ACytotoxicity of NK and CD8+ cells, cytokine expression, cell proliferation, apoptosis, development and differentiation of effector CD8+Brenu et al., 2012 [72]
PlasmaFukuda20 ME/CFS 20 HCMicroRNA profiling by HiSeq2000 sequencing (Illumina HiSeq2000)miR-548j, miR-548ax, miR-127-3p, miR-381-3p, miR-331-3p, miR-136-3p, miR-142-5p, miR-493-5p, miR-143-3p, miR-370, miR-4532miR-126, miR-450b-5p, miR-641, miR-26a-1-3p, miR-3065-3p, miR-5187-3p, miR-16-2-3p, let-7g-3pmiR-127-3p, miR-142-5p, miR-143-3pAutoimmunity, T cell development, cytokine production, inflammatory responses, apoptosisBrenu et al., 2014 [73]
PBMCsFukuda & Canadian35 ME/CFS 50 HCAmbion Bioarray microarrays (version 1 targeting 385 miRNA sequences)let-7b, miR-103, miR-126, miR-145, miR-151, miR-181a, miR-185, miR-191, miR-197, miR-199a, miR-19b, miR-210, miR-22-5p, miR-24, miR-27a, miR-27b, miR-30c, miR-30d, miR-320, miR-324-3p, miR-324-5p, miR-326, miR-330, miR-331-3p, miR-339, miR-422b, miR-423, miR-92, miR-99b miR-99b, miR-30c, miR-126, miR-330-3pAngiogenesis, invasion, migration and proliferation in dendritic cells, proliferative, cytotoxic and cytokine effector function in NK cellsPetty, et al., 2016 [74]
Bolded miRs correspond to miRs DE according to more than one ME/CFS study. Underlined miRs correspond to miRs DE in FM and ME/CFS studies. This table has been adapted from Almenar-Perez, E.; Ovejero, T.; Sánchez-Fito, T.; Espejo, J.A.; Nathanson, L.; Oltra, E. Epigenetic components of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) uncover potential transposable element activation (Clin Ther, accepted, special issue: “Immunology Specialty Update on CFS/ME.”, Elsevier 2019).
Table
Table 3. Classification of drugs commonly prescribed to FM patients.
Table 3. Classification of drugs commonly prescribed to FM patients.
FamilySubfamilyActive PrincipleIUPAC NameReference
AntidepressantsSerotonin-Norepinephrine reuptake inhibitors (SNRIs)Milnacipran(±)-(1R,2S)-rel-2-(Aminomethyl)-N,N-diethyl-1-phenylcyclopropane-1-carboxamideCording M et al., 2015 [76]
Duloxetine(+)-(S)-N-Methyl-3-(naphthalen-1-yloxy)-3-(thiophen-2-yl)propan-1-aminaLunn MP et al., 2014 [77]
Selective serotonin reuptake inhibitors (SSRIs)Citalopram(RS)-1-[3-(dimethylamino) propyl]-1-(4-fluorophenyl)-1,3-dihydroisobenzofuran-5-carbonitrileWalitt B et al., 2015 [78]
Fluoxetine(RS)-N-Methyl-3-phenyl-3-(4-trifluoromethylphenoxy) propylamine
Paroxetine(3S, 4R)-3-[(1,3-Benzodioxol-5-yl oxy) methyl]-4-(4-fluorophenyl) piperidine
Tryptophan2-amino-3-(1H-indol-3-yl) propanoic acid
Escitalopram(S)-1-[3-(Dimethylamino)propyl]-1-(4-fluorophenyl)-1,3-dihydroisobenzofuran-5-carbonitrileRiera R, 2015 [79]
Sertraline(1S,4S)-4-(3,4-dichlorophenyl)-N-methyl-1,2,3,4-tetrahydronaphthalen-1-amine
Tricyclic antidepressantsAmitriptyline8-methyl-2,3,3a,4,5,6-hexahydro-1H-pyrazino[3,2,1-jk]carbazoleMoore RA et al.,2015 [80]
Monoamine oxidase inhibitors (MAOIs)Pirlindole8-methyl-2,3,3a,4,5,6-hexahydro-1H-pyrazino[3,2,1-jk]carbazoleTort S et al., 2012 [81]
Moclobemide4-chloro-N-(2-morpholin-4-ylethyl) benzamide
Mirtazapine(RS)-1,2,3,4,10,14b-Hexahydro-2-methylpyrazino[2,1-a]pyrido[2,3-c][2]benzazepineWelsch P et al., 2018 [82]
Antiepileptics1st GenerationPhenytoin5,5-diphenylimidazolidine-2,4-dioneBirse F et al., 2012 [83]
2nd GenerationValproic acid (Sodium valproate)2-propylpentanoic acidGill D et al., 2011 [84]
Clonazepam5-(2-Chlorophenyl)-7-nitro-1,3-dihydro-1,4-benzodiazepin-2-oneCorrigan R et al., 2012 [85]
3rd GenerationPregabalin(S)-3-(amynomethyl)-5-methylhexanoic acidDerry S et al., 2016 [86]
Gabapentin2-[1-(amynomethyl)cyclohexyl]ethanoic acidWiffen PJ et al., 2017 [87]
LacosamideN2-acetyl-N-benzyl-d-homoserinamideHearn L et al., 2016 [88]
Topiramate2,3: 4,5-Bis-O-(1-methylethylidene)-beta-d-fructopyranose sulfamateWiffen PJ et al., 2013 [89]
AntipsychoticsAtypicalQuetiapine2-(2-(4-dibenzo [b,f] [1,4] thiazepine-11-yl-1-piperazinyl) ethoxy) ethanolWalitt B et al., 2016 (Jun) [90]
CannabinoidsSyntheticNabilone(6aR,10aR)-rel-1-Hydroxy-6,6-dimethyl-3-(2-methyl-2-octanyl)-6,6a,7,8,10,10a-hexahydro-9H-benzo[c]chromen-9-oneWalitt B et al., 2016 (Jul) [91]
Nonsteroidal anti-inflammatory drugs (NSAIDs)Selective inhibitor of Cyclooxygenase 2
(COX-2)		Etoricoxib5-cloro-6′-metil-3-[4-(metilsulfonil)fenil]-2,3′-bipiridineDerry S et al., 2017 [92]
Inhibitor of prostaglandin synthesisIbuprofen(RS)-2-(4-(2-Methylpropyl)phenyl)propanoic acid
Naproxen(+)-(S)-2-(6-Methoxynaphthalen-2-yl)propanoic acid
Inhibitor of Cicloxygenase (COX-1 and COX-2)Tenoxicam(3E)-3-[hydroxy(pyridin-2-ylamino)methylene]-2-methyl-2,3-dihydro-4H-thieno[2,3-e] [1,2]thiazin-4-one 1,1-dioxide
OpioidsSemi syntheticOxycodone(5R,9R,13S,14S)-4,5-α-epoxy-14-hydroxy-3-methoxy-17-methyl-morphinan-6-oneGaskell H et al., 2016 [93]
Drugs commonly prescribed to both FM and ME/CFS patients are bolded.
Table
Table 4. Classification of drugs commonly prescribed to ME/CFS patients.
Table 4. Classification of drugs commonly prescribed to ME/CFS patients.
FamilySubfamilyActive PrincipleIUPAC NameReference
Anticonvulsants3rd GenerationGabapentin2-[1-(amynomethyl)cyclohexyl]ethanoic acidCastro-Marrero J et al., 2017 [48]
Pregabalin(S)-3-(amynomethyl)-5-methylhexanoic acid
AntidepressantsSelective serotonin reuptake inhibitors
(SSRIs)		Nafazodone2-[3-[4-(3-chlorophenyl)piperazin-1-yl]propyl]-5-ethyl-4-(2-phenoxyethyl)-1,2,4-triazol-3-oneCollatz A et al., 2016 [46]
Bupropion(RS)-2-(tert-Butylamino)-1-(3-chlorophenyl)propan-1-oneCastro-Marrero J et al., 2017 [48]
Citalopram((RS)-1-[3-(Dimethylamino)propyl]-1-(4-fluorophenyl)-1,3-dihydroisobenzofuran-5-carbonitrile
Escitalopram((S)-1-[3-(Dimethylamino)propyl]-1-(4-fluorophenyl)-1,3-dihydroisobenzofuran-5-carbonitrile
Fluoxetine(RS)-N-Methyl-3-phenyl-3-(4-trifluoromethylphenoxy) propylamine
Sertraline(1S,4S)-4-(3,4-dichlorophenyl)-N-methyl-1,2,3,4-tetrahydronaphthalen-1-amine
Paroxetine(3S, 4R)-3-[(1,3-Benzodioxol-5-yl oxy) methyl]-4-(4-fluorophenyl) piperidine
Serotonin–norepinephrine reuptake inhibitors (SNRIs)MethylphenidateMethyl phenyl(piperidin-2-yl)acetateBlockmans D and Persoons P, 2016 [97]; Castro-Marrero J et al., 2017 [48]
Duloxetine(+)-(S)-N-Methyl-3-(naphthalen-1-yloxy)-3-(thiophen-2-yl)propan-1-amineCastro-Marrero J et al., 2017 [48]
Venlafaxine(RS)-1-[2-dimethylamino-1-(4-methoxyphenyl)-ethyl]cyclohexanol
Tricyclic antidepressantsAmitriptyline3-(10,11-dihydro-5H-dibenzo [a,d] cycloheptene-5-ylidene)-N, N-dimethyl-1-propanamineCastro-Marrero J et al., 2017 [48]
Clomipramine3-(2-chloro-5,6-dihydrobenzo[b][1]benzazepin-11-yl)-N,N-dimethylpropan-1-amine
Desipramine3-(5,6-dihydrobenzo[b][1]benzazepin-11-yl)-N-methylpropan-1-amine
Doxepin(3E)-3-(6H-benzo[c][1]benzoxepin-11-ylidene)-N,N-dimethylpropan-1-amine
Imipramine3-(5,6-dihydrobenzo[b][1]benzazepin-11-yl)-N,N-dimethylpropan-1-amine
Nortriptyline3-(5,6-dihydrodibenzo[2,1-b:2′,1′-f][7]annulen-11-ylidene)-N-methylpropan-1-amine
Monoamine oxidase inhibitors (MAOIs)Moclobemide4-chloro-N-(2-morpholin-4-ylethyl)benzamideCollatz A et al., 2016 [46];
Castro-Marrero J et al., 2017 [48]
Phenelzine2-phenylethylhydrazine
Selegiline(R)-N-methyl-N-(1-pheny lpropan-2-yl)prop-1-yn-3-amineCastro-Marrero J et al., 2017 [48]
Noradrenergic and specific serotonin antagonist (NaSSAs)Mirtazapine(RS)-1,2,3,4,10,14b-Hexahydro-2-methylpyrazino[2,1-a]pyrido[2,3-c][2]benzazepineCastro-Marrero J et al., 2017 [48]
Monoaminergic stabilizer(–)-OSU-6162(3S)-3-[3-(methylsulfonyl)phenyl]-1-propylpiperidineNilsson MKL et al., 2017 [98]
AntihypertensiveStimulant to α2-ReceptorsClonidine hydrochlorideN-(2,6-dichlorophenyl)-4,5-dihydro-1H-imidazol-2-amine;hydrochlorideCollatz A et al., 2016 [46]
Angiotensin II receptor agonistOlmesartan medoxomil(5-metil-2-oxo-2H-1,3-dioxol-4-il)metil 4-(2-hidroxipropan-2-il)-2-propil-1-({4-[2-(2H-1,2,3,4-tetrazol-5-il)fenil]fenil}metil)-1H-imidazole-5-carboxilatoProal AD et al., 2013 [99]
AntioxidantFatty acid oxidantl-Carnitine3-Hydroxy-4-(trimethylazaniumyl)butanoatePlioplys AV and Plioplys S., 1997 [100]
UbiquinoneCoQ10[(2E,6E,10E,14E,18E,22E,26E,30E,34E)-3,7,11,15,19,23,27,31,35,39-Decamethyltetraconta-2,6,10,14,18,22,26,30,34,38-decaenyl]-5,6-dimethoxy-3-methylcyclohexa-2,5-diene-1,4-dioneCastro-Marrero J et al., 2015 [101]
Re-Dox AgentNADHNicotine adenine dinucleotide
Omega-3 fatty acidα-lipoic acid(R)-5-(1,2-dithiolan-3-yl)pentanoic acidCastro-Marrero J et al., 2017 [48]
Docosahexaenoic acid(DHA)(4Z,7Z,10Z,13Z,16Z,19Z)-docosa-4,7,10,13,16,19-hexaenoic acid
VitaminsVitamin C(2R)-2-[(1S)-1,2-dihydroxyethyl]-3,4-dihydroxy-2H-furan-5-one
Folate (Vitamin B9)(2S)-2-[[4-[(2-Amino-4-oxo-1H-pteridin-6-yl)methylamino]benzoyl]amino]pentanedioic acid
Hydroxycobalamin Vitamin B12)Coα-[α-(5,6-dimethylbenzimidazolyl)]-Coβ-hydroxocobamide
AntiviralBlocking adhesion and viral penetrationAmantadine1-amino-adamantanePlioplys AV and Plioplys S., 1997 [100]
Acid nucleics analogsValganciclovir[2-[(2-amino-6-oxo-3H-purin-9-yl)methoxy]-3-hydroxypropyl] (2S)-2-amino-3-methylbutanoateCollatz A et al., 2016 [46];
Castro-Marrero J et al., 2017 [48]
Acyclovir2-amino-9-(2-hydroxyethoxymethyl)-3H-purin-6-oneCastro-Marrero J et al., 2017 [48]
Valacyclovir2-[(2-amino-6-oxo-3H-purin-9-yl)methoxy]ethyl (2S)-2-amino-3-methylbutanoate
CorticoidsGlucocoticoidHydrocortisone(11β)-11,17,21-trihydroxypregn-4-ene-3,20-dioneBlockmans D et al., 2003 [102];
Collatz A et al., 2016 [46]
Fludrocortisone(8S,9R,10S,11S,13S,14S,17R)-9-fluoro-11,17-dihydroxy-17-(2-hydroxyacetyl)-10,13-dimethyl-1,2,6,7,8,11,12,14,15,16-decahydrocyclopenta[a]phenanthren-3-oneBlockmans D et al., 2003 [102]
Nonsteroidal Anti-Inflammatory Drugs (NSAIDs)Inhibitor of prostaglandin synthesisIbuprofen(RS)-2-(4-(2-Methylpropyl)phenyl)propanoic acidCastro-Marrero J et al., 2017 [48]
Naproxen(+)-(S)-2-(6-Methoxynaphthalen-2-yl)propanoic acid
OthersImmunomodulatory double stranded RNARintatolimod5′-Inosinic acid, homopolymer, complex with 5′-cytidylic acid polymer with 5′-uridylic acid (1:1)Strayer DR et al., 2012 [103]
Anti-neoplasticSodium dichloroacetateDichloroacetic acidComhaire F., 2018 [104]
Ig gamma-1 chain C regionRituximabLithium;4-[2-(diethylamino)ethylcarbamoyl]-2-iodobenzoateCollatz A et al., 2016 [46]
Proliferation inductor from B cellsIntravenous immunoglobulin (Immunoglobulin G)(2S)-2-[[(2S)-1-[(2S)-6-amino-2-[[(2S,3R)-2-[[(2S)-6-amino-2-[[(2S)-2-[[(2S)-4-amino-2-[[(2S)-2-amino-3-(1H-indol-3-yl)propanoyl]amino]-4-oxobutanoyl]amino]propanoyl]amino]hexanoyl]amino]-3-hydroxybutanoyl]amino]hexanoyl]pyrrolidine-2-carbonyl]amino]-5-(diaminomethylideneamino)pentanoic acid
HormoneGrowth hormone (Somatotropin)191 amino acid peptide (IUPAC name N/A)
Wakefulness-promotingModafinil2-[(diphenylmethyl)sulfinul]acetamide
Peripherally-selective antihistamineTerfenadine1-(4-tert-butylphenyl)-4-[4-[hydroxy(diphenyl)methyl]piperidin-1-yl]butan-1-ol
Precursor of CreatineGuanidinoacetic acid (Glycocyamine)2-(diaminomethylideneamino)acetic acidOstojic SM et al., 2016 [105]
PainOpiateCodeine(5α,6α)-7,8-didehydro-4,5-epoxy-3-methoxy-17-methylmorphinan-6-olCastro-Marrero J et al., 2017 [48]
Morphine(4R,4aR,7S,7aR,12bS)-3-Methyl-2,3,4,4a,7,7a-hexahydro-1H-4,12-methanobenzofuro[3,2-e]isoquinoline-7,9-diol
OpiodTramadol(±)-cis-2-[(dimetilamino)metil]-1-(3-metoxifenil) ciclohexanol hidrocloruro
Psycho-pharmaceuticalBenzodiazepineGalantamine hidrobromide(4aS,6R,8aS)-5,6,9,10,11,12-Hexahydro-3-methoxy-11-methyl-4aH-[1]benzofuro[3a,3,2-ef][2]benzazepin-6-olCollatz A et al., 2016 [46]
PsychostimulantDextroamphetamine(2S)-1-phenylpropan-2-amine
Drugs commonly prescribed to both FM and ME/CFS patients are bolded.
Table
Table 5. Effect of FM and ME/CFS polypharmacy on miRNomes associated with disease.
Table 5. Effect of FM and ME/CFS polypharmacy on miRNomes associated with disease.
Prescribed DrugsmiR AffectedDiseasemiR Levels in PatientsTreatment EffectReference
Docosahexaenoic acid (DHA)miR-30cME/CFS↑ (PBMCs) [74]UpregulatedGil-Zamorano J et al., 2014 [106]
miR-143-3pME/CFS↑ (Plasma) [73]Upregulated
miR-181a-5pME/CFS↑ (PBMCs) [74]Upregulated
miR-330ME/CFS↑ (PBMCs) [74]Upregulated
FluoxetinemiR-27bFM↓ (CSF) [67]UpregulatedRodrigues AC et al., 2011 [107]
ME/CFS↑ (PBMCs) [74]
GlucocorticoidmiR-16ME/CFS↓ (Plasma) [73]UpregulatedRainer J et al., 2009 [108]
miR-19bME/CFS↑ (PBMCs) [74]Upregulated
miR-181aME/CFS↑ (PBMCs) [74]UpregulatedRainer J et al., 2009 [108]; Lu S et al., 2012 [109]
miR-223ME/CFS↓ (NK cells) [72]Upregulated
miR-21ME/CFS↓ (NK cells) [72]UpregulatedLu S et al., 2012 [109]
miR-10aME/CFS↓ (NK cells) [72]Upregulated
miR-27aME/CFS↑ (PBMCs) [74]Upregulated
miR-99bME/CFS↑ (PBMCs) [74]Upregulated
miR-126ME/CFS↓ (Plasma) [73]Upregulated
↑ (PBMCs) [74]
miR-145ME/CFS↑ (PBMCs) [74]Upregulated
miR-146aME/CFS↓ (NK cells) [72]Upregulated
miR-324-5pME/CFS↑ (PBMCs) [74]Upregulated
miR-339-3pME/CFS↑ (PBMCs) [74]Upregulated
MorphinemiR-16ME/CFS↓ (Plasma) [73]UpregulatedDave R.S & Khalili K., 2010 [110]
miR-24ME/CFS↑ (PBMCs) [74]Upregulated
miR-30cME/CFS↑ (PBMCs) [74]Upregulated
miR-146aME/CFS↓ (NK cells) [72]Upregulated
miR-21ME/CFS↓ (NK cells) [72]Downregulated
miR-26aME/CFS↓ (NK cells) [72]Downregulated
↑ (PBMCs) [74]
miR-99bME/CFS↑ (PBMCs) [74]Downregulated
miR-191ME/CFS↓ (NK cells) [72]Downregulated
↑ (PBMCs) [74]
miR-320aME/CFS↑ (PBMCs) [74]Downregulated
miR-320cME/CFS↑ (PBMCs) [74]Downregulated
miR-423-5pME/CFS↑ (PBMCs) [74]Downregulated
ValproatemiR-21FM↓ (PBMCs) [69]UpregulatedFayyad-Kazan H et al., 2010 [111]
miR-125aFM↑ (WBC*) [71]Downregulated
* WBC: white blood cells. Bolded miRs correspond to miRs DE according to more than one ME/CFS study. Underlined miRs correspond to mi Rs DE in FM and ME/CFS studies.

Share and Cite

MDPI and ACS Style

Almenar-Pérez, E.; Sánchez-Fito, T.; Ovejero, T.; Nathanson, L.; Oltra, E. Impact of Polypharmacy on Candidate Biomarker miRNomes for the Diagnosis of Fibromyalgia and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Striking Back on Treatments. Pharmaceutics 2019, 11, 126. https://doi.org/10.3390/pharmaceutics11030126

AMA Style

Almenar-Pérez E, Sánchez-Fito T, Ovejero T, Nathanson L, Oltra E. Impact of Polypharmacy on Candidate Biomarker miRNomes for the Diagnosis of Fibromyalgia and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Striking Back on Treatments. Pharmaceutics. 2019; 11(3):126. https://doi.org/10.3390/pharmaceutics11030126

Chicago/Turabian Style

Almenar-Pérez, Eloy, Teresa Sánchez-Fito, Tamara Ovejero, Lubov Nathanson, and Elisa Oltra. 2019. "Impact of Polypharmacy on Candidate Biomarker miRNomes for the Diagnosis of Fibromyalgia and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Striking Back on Treatments" Pharmaceutics 11, no. 3: 126. https://doi.org/10.3390/pharmaceutics11030126

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop