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Metabolomics

, 15:54 | Cite as

The GC–MS metabolomics signature in patients with fibromyalgia syndrome directs to dysbiosis as an aspect contributing factor of FMS pathophysiology

  • Bontle G. Malatji
  • Shayne MasonEmail author
  • Lodewyk J. Mienie
  • Ron A. Wevers
  • Helgard Meyer
  • Mari van Reenen
  • Carolus J. Reinecke
Original Article

Abstract

Introduction

Fibromyalgia syndrome (FMS) is a chronic pain syndrome. Previous analyses of untargeted metabolomics data indicated altered metabolic profile in FMS patients.

Objectives

We report a semi-targeted explorative metabolomics study on the urinary metabolite profile of FMS patients; exploring the potential of urinary metabolite information to augment existing medical diagnosis.

Methods

All cases were females. Patients had a medical history of persistent FMS (n = 18). Control groups were first-generation family members of the patients (n = 11), age-related individuals without indications of FMS (n = 10), and healthy, young (18–22 years) individuals (n = 41). The biofluid investigated was early morning urine samples. Data generation was done through gas chromatography–mass spectrometry (GC–MS) analysis and data processing and analyses were performed using Matlab, R, SPSS and SAS software.

Results

Quantitative analysis revealed the presence of 196 metabolites. Unsupervised and supervised multivariate analyses distinguished all three control groups and the FMS patients, which could be related to 14 significantly increased metabolites. These metabolites are associated with energy metabolism, digestion and metabolism of carbohydrates and other host and gut metabolites.

Conclusions

Overall, urinary metabolite profiles in the FMS patients suggest: (1) energy utilization is a central aspect of this pain disorder, (2) dysbiosis seems to prevail in FMS patients, indicated by disrupted microbiota metabolites, supporting the model that microbiota may alter brain function through the gut-brain axis, with the gut being a gateway to generalized pain, and (3) screening of urine from FMS is an avenue to explore for adding non-invasive clinical information for diagnosis and treatment of FMS.

Keywords

Fibromyalgia syndrome (FMS) Gas chromatography–mass spectrometry (GC–MS) Dysbiosis Carbohydrate Pain Biomarkers 

Abbreviations

2-D-3,5-DHPL

2-Deoxy-3,5-dihydroxypentanoic lactone

2,3,4-Trihydroxy-butyl-L

2,3,4-Trihydroxybutyl-lactone

4-HBA

4-Hydroxybutyric acid

ACR

American College of Rheumatology

ADP

Adenosine-diphosphate

ATP

Adenosine-triphosphate

B–F

Bonferroni–Holm test

BSTFA

N,O-bis-(trimethylsilyl)trifluoraceteamine

CF

Group of first-degree relatives of the patients

CN

Group of healthy young subjects

CO

Group of age-matched subjects but unrelated to the patients

CNS

Central nervous system

EI and HP

A general and a histidine-containing cytoplasmic phosphor-carrier bacterial protein system

EII-AT, EII-BT and EII-CT

Tagatose-specific B. licheniformis multi-domain membrane proteins

EC

Enzyme Commission number

ES

Effect size

FC

Fold change

FIQR

Fibromyalgia impact questionnaire

FM

Fibromyalgia

FMS

Fibromyalgia syndrome

FPS

Functional pain syndromes

G

Galactonic acid-lactone

GC–MS

Gas chromatographic–mass spectrometric

H

2-Hydroxy-glutaric acid

HMDB

Human metabolome database

IBS

Irritable bowel syndrome

IHCQ

In-house clinical questionnaire

M

Malic acid

MW

Mann–Whitney test

NIST

National Institute of Standards and Technology

1H-NMR

Proton nuclear magnetic resonance

NTeMBI

Nuclear Technologies in Medicine and Biosciences Initiative

NWU

North-West University

~P

High-energy phosphate

PC

Principal component

PCA

Principal components analysis

PLS-DA

Partial least squares discriminant analysis

PTS

Phosphoenolpyruvate:carbohydrate phosphotransferase system

Q2

Predictive ability parameter

R2

Goodness-of-fit parameter

SIBO

Small intestinal bacterial over-growth

Tag-1P

Tagatose-1-phosphate

Tag-6P

Tagatose-6-phosphate

TagK

Tagatose-1-phosphate kinase

TMCS

Trimethylchlorosilane

TPs

Tender-points

VIP

Variable important in projection

Notes

Acknowledgements

Research funding for this project was provided by the Technological Innovation Agency (TIA) of the South African Department of Science and Technology (DST) and from the Nuclear Technologies in Medicine and the Biosciences Initiative (NTeMBI) of the Nuclear Energy Corporation of South Africa (NECSA). BM received a postgraduate bursary from the National Research Foundation (NRF) of South Africa.

Author contributions

This investigation required a multidisciplinary approach and the inputs of all authors were essential to produce the concept and final manuscript. CR and HM defined the aim of the study. The urine samples from the FMS patients and age-matched and family-related controls were provided by HM, who also performed all relevant clinical aspects, and acted as assistant promoter to BM. CR developed the experimental design, acted as the promoter for BM and arranged with HM for ethical approval for the study and for the collection of samples from the young controls. BM conducted all experimental analyses and compiled the clinical information provided by HM. LJM gave guidance and assessment on GC–MS data generation and analyses. RW was responsible for critical evaluation of the analytical aspects of the clinical chemistry data and for their interpretation. MvR performed all the statistical analyses. SM was responsible for coordination and integration of inputs from the authors who contributed to the manuscript as presented here.

Compliance with ethical standards

Conflict of interest

The authors declare that there are no competing interests regarding the publication of this paper.

Ethical approval

Ethical approval for the study was obtained via a consortium under the Nuclear Technologies in Medicine and Biosciences Initiative (NTeMBI) of South Africa and ethical approval by Pharma Ethics Pty, Ltd (Reference number 11064365). Pharma Ethics confirmed the following: “The study has been accepted as complying to the Ethics Standards for Clinical Research with a new drug in participants based on FDA, ICH GCP and the Declaration of Helsinki guidelines. The Ethics Committee (IRB) granting this APPROVAL is in compliance with the Guidelines for Good Practice in the Conduct of Clinical Trials in Human Participants in South Africa (2006), ICH Harmonised Tripartite Guidelines E6: Note: for the Guidance in Good Clinical Practice (CPMP/ICH/135/95) and FDA Code of Federal Regulation Part 50, 56 and 312.” Informed consent was obtained from all the participants in this study by means of a voluntarily completed consent form, included in the SI.

Informed consent

All authors have given their approval of the version of the manuscript as submitted, their consent for publication and agreed to the accountability requirements.

Supplementary material

11306_2019_1513_MOESM1_ESM.docx (1.3 mb)
Supplementary material 1 (DOCX 1377 kb)

References

  1. Baumgart, D. C., & Carding, S. R. (2007). Inflammatory bowel disease: Cause and immunobiology. Lancet, 369(9573), 1627–1640.CrossRefGoogle Scholar
  2. Bennett, R. M., Friend, R., Jones, K. D., Ward, R., Han, B. K., & Ross, R. L. (2009). The revised fibromyalgia impact questionnaire (FIQR): Validation and psychometric properties. Arthritis Research & Therapy, 11(4), R120.CrossRefGoogle Scholar
  3. Bouatra, S., Aziat, F., Mandal, R., Guo, A. C., Wilson, M. R., Knox, C., et al. (2013). The human urine metabolome. PLOS ONE, 8(9), e73076.CrossRefGoogle Scholar
  4. Brown, G. K., Cromby, C. H., Manning, N. J., & Pollitt, R. J. (1987). Urinary organic acids in succinic semialdehyde dehydrogenase deficiency: Evidence of α-oxidation of 4-hydroxybutyric acid, interaction of succinic semialdehyde with pyruvate dehydrogenase and possible secondary inhibition of mitochondrial β-oxidation. Journal of Inherited Metabolic Disease, 10(4), 367–375.CrossRefGoogle Scholar
  5. Caboni, P., Liori, B., Kumar, A., Santoru, M. L., Asthana, S., Pieroni, E., et al. (2014). Metabolomics analysis and modeling suggest a Lysophosphocholines-PAF receptor interaction in fibromyalgia. PLoS ONE, 9(9), e107626.CrossRefGoogle Scholar
  6. Carabotti, M., Scirocco, A., Maselli, M. A., & Severi, C. (2015). The gut-brain axis: Interactions between enteric microbiota, central and enteric nervous systems. Annals of Gastroenterology, 28(2), 203–209.PubMedPubMedCentralGoogle Scholar
  7. Clauw, D. J. (2014). Fibromyalgia: A clinical review. JAMA, 311(15), 1547–1555.CrossRefGoogle Scholar
  8. Clauw, D. J. (2015). Fibromyalgia and related conditions. Mayo Clinic Proceedings, 90(5), 680–692.CrossRefGoogle Scholar
  9. Clish, C. B. (2015). Metabolomics: An emerging but powerful tool for precision medicine. Molecular Case Studies, 1(1), a000588.CrossRefGoogle Scholar
  10. Cryan, J. F., & Dinan, T. G. (2012). Mind-altering microorganisms: The impact of the gut microbiota on brain and behaviour. Nature Reviews Neuroscience, 13(10), 701–712.CrossRefGoogle Scholar
  11. Cryan, J. F., & O’Mahony, S. M. (2011). The microbiome-gut-brain axis: From bowel to behavior. Journal of Neurogastroenterology and Motility, 23(3), 187–192.CrossRefGoogle Scholar
  12. Egloff, N., Von Känel, R., Müller, V., Egle, U. T., Kokinogenis, G., Lederbogen, S., et al. (2015). Implications of proposed fibromyalgia criteria across other functional pain syndromes. Scandinavian Journal of Rheumatology, 44(5), 416–424.CrossRefGoogle Scholar
  13. Foster, J. A., & Neufeld, K. A. (2013). Gut–brain axis: How the microbiome influences anxiety and depression. Trends in Neurosciences, 36(5), 305–312.CrossRefGoogle Scholar
  14. Ge, Y., Duan, Y., Fang, G., Zhang, Y., & Wang, S. (2009). Polysaccharides from fruit calyx of Physalis alkekengi var. francheti: Isolation, purification, structural features and antioxidant activities. Carbohydrate Polymers, 77(2), 188–193.CrossRefGoogle Scholar
  15. Hackshaw, K. V., Rodriguez-Saona, L., Plans, M., Bell, L. N., & Buffington, C. T. (2013). A bloodspot-based diagnostic test for fibromyalgia syndrome and related disorders. Analyst, 138(16), 4453–4462.CrossRefGoogle Scholar
  16. Hansen, J., Gulati, A., & Sartor, R. B. (2010). The role of mucosal immunity and host genetics in defining intestinal commensal bacteria. Current Opinion in Gastroenterology, 26(6), 564–571.CrossRefGoogle Scholar
  17. Henström, M., & D’Amato, M. (2016). Genetics of irritable bowel syndrome. Molecular and Cellular Neuroscience, 3(1), 7.Google Scholar
  18. Högenauer, C., Hammer, H. F., Krejs, G. J., & Reisinger, C. (1998). Mechanisms and management of antibiotic-associated diarrhea. Clinical Infectious Diseases, 27(4), 702–710.CrossRefGoogle Scholar
  19. Isono, M., Nakanishi, I., Sasajima, K. I., Motizuki, K., Kanzaki, T., Okazaki, H., et al. (1968). 2-Keto-l-gulonic acid fermentation: Part I. Paper chromatographic characterization of metabolic products from sorbitol and l-sorbose by various bacteria. Agricultural Biological Chemistry, 32(4), 424–431.Google Scholar
  20. Jackson, F., Georgakopoulou, N., Kaluarachchi, M., Kyriakides, M., Andreas, N., Przysiezna, N., et al. (2016). Development of a pipeline for exploratory metabolic profiling of infant urine. Journal of Proteome Research, 15(9), 3432–3440.CrossRefGoogle Scholar
  21. Jones, K. D., Gelbart, T., Whisenant, T. C., Waalen, J., Mondala, T. S., Iklé, D. N., et al. (2016). Genome-wide expression profiling in the peripheral blood of patients with fibromyalgia. Clinical and Experimental Rheumatology, 34(2 Suppl 96), 89–98.PubMedCentralGoogle Scholar
  22. Kelker, N. E., Simkins, R. A., & Anderson, R. L. (1972). Pathway of l-sorbose metabolism in Aerobacter aerogenes. Journal of Biological Chemistry, 247(5), 1479–1483.PubMedGoogle Scholar
  23. Kennedy, P. J., Cryan, J. F., Dinan, T. G., & Clarke, G. (2014). Irritable bowel syndrome: A microbiome-gut-brain axis disorder? World Journal of Gastroenterology, 20(39), 14105–14125.CrossRefGoogle Scholar
  24. Kundig, W., Ghosh, S., & Roseman, S. (1964). Phosphate bound to histidine in a protein as an intermediate in a novel phospho-transferase system. PNAS, 52(4), 1067–1074.CrossRefGoogle Scholar
  25. Legangneux, E., Mora, J. J., Spreux-Varoquaux, O., Thorin, I., Herrou, M., Alvado, G., et al. (2001). Cerebrospinal fluid biogenic amine metabolites, plasma-rich platelet serotonin and [3H]imipramine reuptake in the primary fibromyalgia syndrome. Rheumatology, 40, 290–296.CrossRefGoogle Scholar
  26. Levine, J. S., & Burakoff, R. (2011). Extraintestinal manifestations of inflammatory bowel disease. Gastroenterology & Hepatology (NY), 7(4), 235–241.Google Scholar
  27. Li, M., Wang, B., Zhang, M., Rantalainen, M., Wang, S., Zhou, H., et al. (2008). Symbiotic gut microbes modulate human metabolic phenotypes. PNAS, 105(6), 2117–2122.CrossRefGoogle Scholar
  28. Malatji, B. G., Meyer, H., Mason, S., Engelke, U. F., Wevers, R. A., van Reenen, M., et al. (2017). A diagnostic biomarker profile for fibromyalgia syndrome based on an NMR metabolomics study of selected patients and controls. BMC Neurology, 17(1), 88.CrossRefGoogle Scholar
  29. Mason, S., Moutloatse, G. P., van Furth, M. A., Solomons, R., van Reenen, M., Reinecke, C. J., et al. (2014). KEMREP: A new qualitative method for the assessment of an analyst’s ability to generate a metabolomics data matrix by gas chromatography–mass spectrometry. Current Metabolomics, 2(1), 15–26.CrossRefGoogle Scholar
  30. Mayer, E. A., & Tillisch, K. (2011). The brain-gut axis in abdominal pain syndromes. Annual Review of Medicine, 62, 381–396.CrossRefGoogle Scholar
  31. Mayer, E. A., Tillisch, K., & Gupta, A. (2015). Gut/brain axis and the microbiota. Journal of Clinical Investigation, 125(3), 926–938.CrossRefGoogle Scholar
  32. McFarland, L. V. (2014). Use of probiotics to correct dysbiosis of normal microbiota following disease or disruptive events: A systematic review. British Medical Journal Open, 4(8), e005047.Google Scholar
  33. Medina, S., Domínguez-Perles, R., Ferreres, F., Tomás-Barberán, F. A., & Gil-Izquierdo, Á. (2013). The effects of the intake of plant foods on the human metabolome. Trends in Analytical Chemistry, 52, 88–99.CrossRefGoogle Scholar
  34. Meyer, H. P. (2002). Myofascial pain syndrome and its suggested role in the pathogenesis and treatment of fibromyalgia syndrome. Current Pain and Headache Reports, 6(4), 274–283.CrossRefGoogle Scholar
  35. Miquel, S., Leclerc, M., Martin, R., Chain, F., Lenoir, M., Raguideau, S., et al. (2015). Identification of metabolic signatures linked to anti-inflammatory effects of Faecalibacterium prausnitzii. mBio, 6(2), e00300-15.CrossRefGoogle Scholar
  36. Moon, J. Y., Jung, H. J., Moon, M. H., Chung, B. C., & Choi, M. H. (2009). Heat-map visualization of gas chromatography-mass spectrometry based quantitative signatures on steroid metabolism. Journal of the American Society for Mass Spectrometry, 20(9), 1626–1637.CrossRefGoogle Scholar
  37. Murphey, W. H., & Rosenblum, E. D. (1964). Mannitol catabolism by Staphylococcus aureus. Archives of Biochemistry and Biophysics, 107(2), 292–297.CrossRefGoogle Scholar
  38. Nicholson, J. K., Holmes, E., Kinross, J., Burcelin, R., Gibson, G., Jia, W., et al. (2012). Host-gut microbiota metabolic interactions. Science, 336(6086), 1262–1267.CrossRefGoogle Scholar
  39. Nicholson, J. K., Holmes, E., & Wilson, I. D. (2005). Gut microorganisms, mammalian metabolism and personalized health care. Nature Reviews Microbiology, 3(5), 431–438.CrossRefGoogle Scholar
  40. Pimentel, M., Chow, E. J., Hallegua, D., Wallace, D., & Lin, H. C. (2001). Small intestinal bacterial overgrowth: A possible association with fibromyalgia. Journal of Musculoskeletal Pain, 9(3), 105–113.CrossRefGoogle Scholar
  41. Postma, P. W., & Lengeler, J. W. (1985). Phosphoenolpyruvate: Carbohydrate phosphotransferase system of bacteria. Microbiological Reviews, 49(3), 232–269.PubMedPubMedCentralGoogle Scholar
  42. Russell, I. J., Orr, M. D., Littman, B., Vipraio, G. A., Alboukrek, D., Michalek, J. E., et al. (1994). Elevated cerebrospinal fluid levels of substance P in patients with the fibromyalgia syndrome. Arthritis & Rheumatology, 37(11), 1593–1601.CrossRefGoogle Scholar
  43. Saier, M. H., Jr. (2015). The bacterial phosphotransferase system: New frontiers 50 years after its discovery. Journal of Molecular Microbiology and Biotechnology, 25(2–3), 73–78.CrossRefGoogle Scholar
  44. Shah, J. P., Phillips, T. M., Danoff, J. V., & Gerber, L. H. (2005). An in vivo microanalytical technique for measuring the local biochemical milieu of human skeletal muscle. Journal of Applied Physiology, 99(5), 1977–1984.CrossRefGoogle Scholar
  45. van der Heiden, E., Delmarcelle, M., Lebrun, S., Freichels, R., Brans, A., Vastenavond, C. M., et al. (2013). A pathway closely related to the d-tagatose pathway of Gram-negative enterobacteria identified in the Gram-positive bacterium Bacillus licheniformis. Applied and Environmental Microbiology, 79(11), 3511–3515.CrossRefGoogle Scholar
  46. van Schaik, W., & Willems, R. J. (2010). Genome-based insights into the evolution of enterococci. Clinical Microbiology & Infection, 16(6), 527–532.CrossRefGoogle Scholar
  47. Wallace, D. J., & Hallegua, D. S. (2004). Fibromyalgia: The gastrointestinal link. Current Pain and Headache Reports, 8(5), 364–368.CrossRefGoogle Scholar
  48. Willats, W. G. T., McCartney, L., Mackie, W., & Knox, J. P. (2001). Pectin: Cell biology and prospects for functional analysis. Plant Molecular Biology, 47(1–2), 9–27.CrossRefGoogle Scholar
  49. Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., et al. (2012). HMDB 3.0—The human metabolome database in 2013. Nucleic Acids Research, 41(D1), D801–D807.CrossRefGoogle Scholar
  50. Wolfe, F., Smythe, H. A., Yunus, M. B., Bennett, R. M., Bombardier, C., Goldenberg, D. L., et al. (1990). The American College of Rheumatology 1990 criteria for the classification of fibromyalgia. Arthritis & Rheumatology, 33(2), 160–172.CrossRefGoogle Scholar
  51. Yunus, M. B., Dailey, J. W., Aldag, J. C., Masi, A. T., & Jobe, P. C. (1992). Plasma and urinary catecholamines in primary fibromyalgia: A controlled study. Journal of Rheumatology, 19, 95–97.PubMedGoogle Scholar

Copyright information

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Authors and Affiliations

  1. 1.Faculty of Natural and Agricultural Sciences, Centre for Human MetabolomicsNorth-West University (Potchefstroom Campus)PotchefstroomSouth Africa
  2. 2.Translational Metabolic Laboratory, Department of Laboratory MedicineRadboud University Nijmegen Medical CentreNijmegenThe Netherlands
  3. 3.Department of Family Medicine, Kalafong HospitalUniversity of PretoriaPretoriaSouth Africa

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