Comprehensive Circulatory Metabolomics in ME/CFS Reveals Disrupted Metabolism of Acyl Lipids and Steroids

Arnaud Germain, Dinesh K Barupal, Susan M Levine, Maureen R Hanson, Arnaud Germain, Dinesh K Barupal, Susan M Levine, Maureen R Hanson

Abstract

The latest worldwide prevalence rate projects that over 65 million patients suffer from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), an illness with known effects on the functioning of the immune and nervous systems. We performed an extensive metabolomics analysis on the plasma of 52 female subjects, equally sampled between controls and ME/CFS patients, which delivered data for about 1750 blood compounds spanning 20 super-pathways, subdivided into 113 sub-pathways. Statistical analysis combined with pathway enrichment analysis points to a few disrupted metabolic pathways containing many unexplored compounds. The most intriguing finding concerns acyl cholines, belonging to the fatty acid metabolism sub-pathway of lipids, for which all compounds are consistently reduced in two distinct ME/CFS patient cohorts. We compiled the extremely limited knowledge about these compounds and regard them as promising in the quest to explain many of the ME/CFS symptoms. Another class of lipids with far-reaching activity on virtually all organ systems are steroids; androgenic, progestin, and corticosteroids are broadly reduced in our patient cohort. We also report on lower dipeptides and elevated sphingolipids abundance in patients compared to controls. Disturbances in the metabolism of many of these molecules can be linked to the profound organ system symptoms endured by ME/CFS patients.

Keywords: ME/CFS; acyl cholines; dipeptides; lipids; metabolomics; steroids.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Box plot distribution of logged values for the metabolites that are part of the acyl choline pathway. Controls (CTRL) are shown in red and patients (myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)) in blue. The yellow diamond represents the mean. (a) Arachidonoyl-choline, (b) Dihomolinoleoyl-choline, (c) Docosahexaenoyl-choline, (d) Linoleoyl-choline, (e) Oleoyl-choline, (f) Palmitoyl-choline, (g) Stearoyl-choline.
Figure 2
Figure 2
Box plot distribution of concentrations for the metabolites in Table 3. Controls (CTRL) are shown in red and patients (ME/CFS) in blue. The yellow diamond represents the mean. (a) CER(18:0), (b) CER(18:1), (c) CER(20:0).
Figure 3
Figure 3
Display of the fold change (controls/patients) of the median, averaged for each of the 94 sub-pathways. Roman numbers at the bottom of the figure are assigned as follow for each of the nine super-pathways: I = amino acids, II = carbohydrates, III = cofactors and vitamins, IV = energy, V = lipids, VI = nucleotides, VII = partially characterized molecules, VIII = peptides, and IX = xenobiotics. Labelled sub-pathways are discussed in the manuscript; brown ones are over-abundant in controls compared to patients while purple ones are the opposite. The number associated with each sub-pathway reflects the number of metabolites included. Omitted from the graph are eight drug sub-pathways as well as the tobacco metabolites, all classified as xenobiotics.

References

    1. Valdez A.R., Hancock E.E., Adebayo S., Kiernicki D.J., Proskauer D., Attewell J.R., Bateman L., DeMaria A., Lapp C.W., Rowe P.C., et al. Estimating prevalence, demographics, and costs of ME/CFS using large scale medical claims data and machine learning. Front. Pediatr. 2019;6:412. doi: 10.3389/fped.2018.00412.
    1. Cliff J.M., King E.C., Lee J.S., Sepulveda N., Wolf A.S., Kingdon C., Bowman E., Dockrell H.M., Nacul L., Lacerda E., et al. Cellular immune function in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) Front. Immunol. 2019;10:796. doi: 10.3389/fimmu.2019.00796.
    1. Rivas J.L., Palencia T., Fernandez G., Garcia M. Association of T and NK cell phenotype with the diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) Front. Immunol. 2018;9:1028. doi: 10.3389/fimmu.2018.01028.
    1. Maes M., Bosmans E., Kubera M. Increased expression of activation antigens on CD8+T lymphocytes in Myalgic Encephalomyelitis/chronic fatigue syndrome: Inverse associations with lowered CD19+expression and CD4+/CD8+ratio, but no associations with (auto)immune, leaky gut, oxidative and nitrosative stress biomarkers. Neuroendocrinol. Lett. 2015;36:439–446.
    1. Klimas N.G., Salvato F.R., Morgan R., Fletcher M.A. Immunological abnormalities in chronic fatigue syndrome. J. Clin. Microbiol. 1990;28:1403–1410. doi: 10.1128/JCM.28.6.1403-1410.1990.
    1. Hornig M., Montoya J.G., Klimas N.G., Levine S., Felsenstein D., Bateman L., Peterson D.L., Gottschalk C.G., Schultz A.F., Che X., et al. Distinct plasma immune signatures in ME/CFS are present early in the course of illness. Sci. Adv. 2015;1:e1400121. doi: 10.1126/sciadv.1400121.
    1. Montoya J.G., Holmes T.H., Anderson J.N., Maecker H.T., Rosenberg-Hasson Y., Valencia I.J., Chu L., Younger J.W., Tato C.M., Davis M.M. Cytokine signature associated with disease severity in chronic fatigue syndrome patients. Proc. Natl. Acad. Sci. USA. 2017;114:E7150–E7158. doi: 10.1073/pnas.1710519114.
    1. Boissoneault J., Letzen J., Lai S., O’Shea A., Craggs J., Robinson M.E., Staud R. Abnormal resting state functional connectivity in patients with chronic fatigue syndrome: An arterial spin-labeling fMRI study. Magn. Reson. Imaging. 2016;34:603–608. doi: 10.1016/j.mri.2015.12.008.
    1. Gay C.W., Robinson M.E., Lai S., O’Shea A., Craggs J.G., Price D.D., Staud R. Abnormal resting-state functional connectivity in patients with chronic fatigue syndrome: Results of seed and data-driven analyses. Brain Connect. 2016;6:48–56. doi: 10.1089/brain.2015.0366.
    1. Boissoneault J., Letzen J., Robinson M., Staud R. Cerebral blood flow and heart rate variability predict fatigue severity in patients with chronic fatigue syndrome. Brain Imaging Behav. 2019;13:789–797. doi: 10.1007/s11682-018-9897-x.
    1. Aaron L.A., Herrell R., Ashton S., Belcourt M., Schmaling K., Goldberg J., Buchwald D. Comorbid clinical conditions in chronic fatigue—A co-twin control study. J. Gen. Intern. Med. 2001;16:24–31. doi: 10.1111/j.1525-1497.2001.03419.x.
    1. Nagy-Szakal D., Williams B.L., Mishra N., Che X., Lee B., Bateman L., Klimas N.G., Komaroff A.L., Levine S., Montoya J.G., et al. Fecal metagenomic profiles in subgroups of patients with myalgic encephalomyelitis/chronic fatigue syndrome. Microbiome. 2017;5:44. doi: 10.1186/s40168-017-0261-y.
    1. Armstrong C.W., McGregor N.R., Lewis D.P., Butt H.L., Gooley P.R. Metabolic profiling reveals anomalous energy metabolism and oxidative stress pathways in chronic fatigue syndrome patients. Metabolomics. 2015;11:1626–1639. doi: 10.1007/s11306-015-0816-5.
    1. Armstrong C.W., McGregor N.R., Lewis D.P., Butt H.L., Gooley P.R. The association of fecal microbiota and fecal, blood serum and urine metabolites in myalgic encephalomyelitis/chronic fatigue syndrome. Metabolomics. 2017;13:8. doi: 10.1007/s11306-016-1145-z.
    1. Fluge O., Mella O., Bruland O., Risa K., Dyrstad S.E., Alme K., Rekeland I.G., Sapkota D., Rosland G.V., Fossa A., et al. Metabolic profiling indicates impaired pyruvate dehydrogenase function in myalgic encephalopathy/chronic fatigue syndrome. JCI Insight. 2016;1:e89376. doi: 10.1172/jci.insight.89376.
    1. Naviaux R.K., Naviaux J.C., Li K.F., Bright A.T., Alaynick W.A., Wang L., Baxter A., Nathan N., Anderson W., Gordon E. Metabolic features of chronic fatigue syndrome. Proc. Natl. Acad. Sci. USA. 2016;113:E5472–E5480. doi: 10.1073/pnas.1607571113.
    1. Yamano E., Sugimoto M., Hirayama A., Kume S., Yamato M., Jin G.H., Tajima S., Goda N., Iwai K., Fukuda S., et al. Index markers of chronic fatigue syndrome with dysfunction of TCA and urea cycles. Sci. Rep. 2016;6:34990. doi: 10.1038/srep34990.
    1. Nagy-Szakal D., Barupal D.K., Lee B., Che X., Williams B.L., Kahn E.J.R., Ukaigwe J.E., Bateman L., Klimas N.G., Komaroff A.L., et al. Insights into myalgic encephalomyelitis/chronic fatigue syndrome phenotypes through comprehensive metabolomics. Sci. Rep. 2018;8:10056. doi: 10.1038/s41598-018-28477-9.
    1. Germain A., Ruppert D., Levine S.M., Hanson M.R. Metabolic profiling of a myalgic encephalomyelitis/chronic fatigue syndrome discovery cohort reveals disturbances in fatty acid and lipid metabolism. Mol. Biosyst. 2017;13:371–379. doi: 10.1039/C6MB00600K.
    1. Germain A., Ruppert D., Levine S.M., Hanson M.R. Prospective biomarkers from plasma metabolomics of myalgic encephalomyelitis/chronic fatigue syndrome implicate redox imbalance in disease symptomatology. Metabolites. 2018;8:90. doi: 10.3390/metabo8040090.
    1. McGregor N.R., Armstrong C.W., Lewis D.P., Gooley P.R. Post-exertional malaise is associated with hypermetabolism, hypoacetylation and purine metabolism deregulation in ME/CFS cases. Diagnostics. 2019;9:70. doi: 10.3390/diagnostics9030070.
    1. Ware J.E., Kosinski M. Interpreting SF-36 summary health measures: A response. Qual. Life Res. 2001;10:405–413. doi: 10.1023/A:1012588218728.
    1. Moghimipour E., Ameri A., Handali S. Absorption-enhancing effects of bile salts. Molecules. 2015;20:14451–14473. doi: 10.3390/molecules200814451.
    1. Hanson M.R., Giloteaux L. The gut microbiome in myalgic encephalomyelitis. Biochemist. 2017;39:10–13. doi: 10.1042/BIO03902010.
    1. Sperringer J.E., Addington A., Hutson S.M. Branched-chain amino acids and brain metabolism. Neurochem. Res. 2017;42:1697–1709. doi: 10.1007/s11064-017-2261-5.
    1. Cruzat V.F., Bittencourt A., Scomazzon S.P., Leite J.S.M., de Bittencourt P.I.H., Tirapegui J. Oral free and dipeptide forms of glutamine supplementation attenuate oxidative stress and inflammation induced by endotoxemia. Nutrition. 2014;30:602–611. doi: 10.1016/j.nut.2013.10.019.
    1. Ano Y., Kita M., Kitaoka S., Furuyashiki T. Leucine-histidine dipeptide attenuates microglial activation and emotional disturbances induced by brain inflammation and repeated social defeat stress. Nutrients. 2019;11:2161. doi: 10.3390/nu11092161.
    1. Summers S.A., Chaurasia B., Holland W.L. Metabolic messengers: Ceramides. Nat. Metab. 2019 doi: 10.1038/s42255-019-0134-8.
    1. Mitsnefes M.M., Fitzpatrick J., Sozio S.M., Jaar B.G., Estrella M.M., Monroy-Trujillo J.M., Zhang W.J., Setchell K., Parekh R.S. Plasma glucosylceramides and cardiovascular risk in incident hemodialysis patients. J. Clin. Lipidol. 2018;12:1513–1522. doi: 10.1016/j.jacl.2018.07.011.
    1. Hannun Y.A., Obeid L.M. Sphingolipids and their metabolism in physiology and disease. Nat. Rev. Mol. Cell Biol. 2018;19:175–191. doi: 10.1038/nrm.2017.107.
    1. Prough R.A., Clark B.J., Klinge C.M. Novel mechanisms for DHEA action. J. Mol. Endocrinol. 2016;56:R139–R155. doi: 10.1530/JME-16-0013.
    1. de Kloet E.R., Meijer O.C., de Nicola A.F., de Rijk R.H., Joels M. Importance of the brain corticosteroid receptor balance in metaplasticity, cognitive performance and neuro-inflammation. Front. Neuroendocr. 2018;49:124–145. doi: 10.1016/j.yfrne.2018.02.003.
    1. Shao Y.P., Le W.D. Recent advances and perspectives of metabolomics-based investigations in Parkinson’s disease. Mol. Neurodegener. 2019;14:3. doi: 10.1186/s13024-018-0304-2.
    1. Hunt R., de Mortemer Taveau R. The Effects of a Number of Derivatives of Choline and Analogous Compounds of the Blood-Pressure. US Government Printing Office; Washington, DC, USA: 1911. p. 73.
    1. Schneider R., Timms A.R., Kyi Z.Y., Wilson W. Some aspects of the pharmacology of an homologous series of choline esters of fatty acids. Br. J. Pharmacol. Chemother. 1956;12:30–38. doi: 10.1111/j.1476-5381.1957.tb01358.x.
    1. IOM . Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness. The National Academies Press; Washington, DC, USA: 2015.
    1. Cornelis M.C., Erlund I., Michelotti G.A., Herder C., Westerhuis J.A., Tuomilehto J. Metabolomic response to coffee consumption: Application to a three-stage clinical trial. J. Intern. Med. 2018;283:544–557. doi: 10.1111/joim.12737.
    1. Kuang A., Erlund I., Herder C., Westerhuis J.A., Tuomilehto J., Cornelis M.C. Lipidomic response to coffee consumption. Nutrients. 2018;10:1851. doi: 10.3390/nu10121851.
    1. Li K.J., Borresen E.C., Jenkins-Puccetti N., Luckasen G., Ryan E.P. Navy bean and rice bran intake alters the plasma metabolome of children at risk for cardiovascular disease. Front. Nutr. 2018;4:71. doi: 10.3389/fnut.2017.00071.
    1. Zarei I., Oppel R.C., Borresen E.C., Brown R.J., Ryan E.P. Modulation of plasma and urine metabolome in colorectal cancer survivors consuming rice bran. Integr. Food Nutr. Metab. 2019;6 doi: 10.15761/IFNM.1000252.
    1. Lujuan X., MacKenzie E.C., Hua Z., Wangang Z., Yoshinori M. Carnosine—A natural bioactive dipeptide: Bioaccessibility, bioavailability and health benefits. J. Food Bioact. 2019;5 doi: 10.31665/JFB.2019.5174.
    1. Raizel R., Tirapegui J. Role of glutamine, as free or dipeptide form, on muscle recovery from resistance training: A review study. Nutrire. 2018;43:28. doi: 10.1186/s41110-018-0087-9.
    1. Ano Y., Yoshino Y., Uchida K., Nakayama H. Preventive effects of tryptophan-methionine dipeptide on neural inflammation and alzheimer’s pathology. Int. J. Mol. Sci. 2019;20:3206. doi: 10.3390/ijms20133206.
    1. Cole T.J., Short K.L., Hooper S.B. The science of steroids. Semin. Fetal Neonatl Med. 2019;24:170–175. doi: 10.1016/j.siny.2019.05.005.
    1. Baulieu E.-E., Robel P. Dehydroepiandrosterone (DHEA) and dehydroepiandrosterone sulfate (DHEAS) as neuroactive neurosteroids. Proc. Natl. Acad. Sci. USA. 1998;95:4089–4091. doi: 10.1073/pnas.95.8.4089.
    1. 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. doi: 10.1111/bph.13702.
    1. de Vega W.C., Herrera S., Vernon S.D., McGowan P.O. Epigenetic modifications and glucocorticoid sensitivity in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) BMC Med. Genom. 2017;10:11. doi: 10.1186/s12920-017-0248-3.
    1. 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. doi: 10.7326/0003-4819-121-12-199412150-00009.
    1. Ware J.E., Sherbourne C.D. The Mos 36-Item Short-Form Health Survey (Sf-36). 1. Conceptual-Framework and Item Selection. Med. Care. 1992;30:473–483. doi: 10.1097/00005650-199206000-00002.
    1. Ware J.E., Jr., Kosinski M., Bayliss M.S., McHorney C.A., Rogers W.H., Raczek A. Comparison of methods for the scoring and statistical analysis of SF-36 health profile and summary measures: Summary of results from the Medical Outcomes Study. Med. Care. 1995;33:AS264–AS279.
    1. Chong J., Soufan O., Li C., Caraus I., Li S., Bourque G., Wishart D.S., Xia J. MetaboAnalyst 4.0: Towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 2018;46:W486–W494. doi: 10.1093/nar/gky310.
    1. Barupal D.K., Fiehn O. Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets. Sci. Rep. 2017;7:14567. doi: 10.1038/s41598-017-15231-w.

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