Metabolomic Evidence for Peroxisomal Dysfunction in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Xiaoyu Che, Christopher R Brydges, Yuanzhi Yu, Adam Price, Shreyas Joshi, Ayan Roy, Bohyun Lee, Dinesh K Barupal, Aaron Cheng, Dana March Palmer, Susan Levine, Daniel L Peterson, Suzanne D Vernon, Lucinda Bateman, Mady Hornig, Jose G Montoya, Anthony L Komaroff, Oliver Fiehn, W Ian Lipkin, Xiaoyu Che, Christopher R Brydges, Yuanzhi Yu, Adam Price, Shreyas Joshi, Ayan Roy, Bohyun Lee, Dinesh K Barupal, Aaron Cheng, Dana March Palmer, Susan Levine, Daniel L Peterson, Suzanne D Vernon, Lucinda Bateman, Mady Hornig, Jose G Montoya, Anthony L Komaroff, Oliver Fiehn, W Ian Lipkin

Abstract

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic and debilitating disease characterized by unexplained physical fatigue, cognitive and sensory dysfunction, sleeping disturbances, orthostatic intolerance, and gastrointestinal problems. People with ME/CFS often report a prodrome consistent with infections. Using regression, Bayesian and enrichment analyses, we conducted targeted and untargeted metabolomic analysis of plasma from 106 ME/CFS cases and 91 frequency-matched healthy controls. Subjects in the ME/CFS group had significantly decreased levels of plasmalogens and phospholipid ethers (p < 0.001), phosphatidylcholines (p < 0.001) and sphingomyelins (p < 0.001), and elevated levels of dicarboxylic acids (p = 0.013). Using machine learning algorithms, we were able to differentiate ME/CFS or subgroups of ME/CFS from controls with area under the receiver operating characteristic curve (AUC) values up to 0.873. Our findings provide the first metabolomic evidence of peroxisomal dysfunction, and are consistent with dysregulation of lipid remodeling and the tricarboxylic acid cycle. These findings, if validated in other cohorts, could provide new insights into the pathogenesis of ME/CFS and highlight the potential use of the plasma metabolome as a source of biomarkers for the disease.

Keywords: biomarker; chronic fatigue syndrome; cytidine-5′-diphosphocholine pathway; metabolomics; myalgic encephalomyelitis; peroxisome; tricarboxylic acid cycle.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Pipeline for sample selection. EC: Exclusion criteria; WP: Withdrew participation; LFU: Loss to follow up; SSETR: Study site enrollment target reached; FBS: Failed baseline screening; MC: Medical conditions; FMC: Frequency matching criteria.
Figure 2
Figure 2
Chemical enrichment analyses using ChemRICH. HEPE: hydroxy eicosapentaenoic acid; ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome; sr-IBS: self-reported physician diagnosed irritable bowel syndrome. The length of the bar represents altered ratio for each metabolic cluster. A bar restricted to the left of the centered vertical line indicates a metabolic cluster that is lower in ME/CFS patients. A bar restricted to the right of the centered vertical line indicates a metabolic cluster that is higher in ME/CFS patients. A bar that crosses the vertical line indicates a metabolic cluster that is dysregulated in mixed directions. (A) All ME/CFS vs. controls. (B) Female ME/CFS vs. female controls. (C) ME/CFS without sr-IBS vs. controls without sr-IBS.
Figure 2
Figure 2
Chemical enrichment analyses using ChemRICH. HEPE: hydroxy eicosapentaenoic acid; ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome; sr-IBS: self-reported physician diagnosed irritable bowel syndrome. The length of the bar represents altered ratio for each metabolic cluster. A bar restricted to the left of the centered vertical line indicates a metabolic cluster that is lower in ME/CFS patients. A bar restricted to the right of the centered vertical line indicates a metabolic cluster that is higher in ME/CFS patients. A bar that crosses the vertical line indicates a metabolic cluster that is dysregulated in mixed directions. (A) All ME/CFS vs. controls. (B) Female ME/CFS vs. female controls. (C) ME/CFS without sr-IBS vs. controls without sr-IBS.
Figure 3
Figure 3
ME/CFS predictive modeling. ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome; sr-IBS: self-reported physician diagnosed irritable bowel syndrome; BF: BayesFactor; AUC: area under the receiver operating characteristic curve. To differentiate ME/CFS cases from healthy controls, we employed five machine learning algorithms: least absolute shrinkage and selection operator (Lasso), adaptive Lasso (AdaLasso), Random Forests (RF), XGBoost, and Bayesian Model Averaging (Model average). For each algorithm, three sets of predictors were considered: (1) all metabolites, (2) metabolites with BayesFactor > 1, and (3) metabolites with BayesFactor > 3. The predictive models were first trained in the 80% randomly selected training set using 10-fold cross-validation, and the remaining 20% of the study population was used as the independent test set to validate model performance. (A) Overall population. (B) Women only. (C) No GI complaints.
Figure 3
Figure 3
ME/CFS predictive modeling. ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome; sr-IBS: self-reported physician diagnosed irritable bowel syndrome; BF: BayesFactor; AUC: area under the receiver operating characteristic curve. To differentiate ME/CFS cases from healthy controls, we employed five machine learning algorithms: least absolute shrinkage and selection operator (Lasso), adaptive Lasso (AdaLasso), Random Forests (RF), XGBoost, and Bayesian Model Averaging (Model average). For each algorithm, three sets of predictors were considered: (1) all metabolites, (2) metabolites with BayesFactor > 1, and (3) metabolites with BayesFactor > 3. The predictive models were first trained in the 80% randomly selected training set using 10-fold cross-validation, and the remaining 20% of the study population was used as the independent test set to validate model performance. (A) Overall population. (B) Women only. (C) No GI complaints.
Figure 4
Figure 4
Correlation heatmap. MFI, Multidimensional Fatigue Inventory scored on 0–100 scale with 0 = no fatigue and 100 = maximal fatigue. ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome. * p < 0.01. Heatmap showing the correlation coefficients between the plasma levels of metabolites in the metabolic clusters that were significantly altered in ME/CFS (bold in Supplementary Table S4) and MFI scales using Spearman’s correlation tests in all ME/CFS, all controls, female ME/CFS, female controls, male ME/CFS, and male controls. (A) Correlation heatmap, part 1. (B) Correlation heatmap, part 2. (C) Correlation heatmap, part 3.
Figure 4
Figure 4
Correlation heatmap. MFI, Multidimensional Fatigue Inventory scored on 0–100 scale with 0 = no fatigue and 100 = maximal fatigue. ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome. * p < 0.01. Heatmap showing the correlation coefficients between the plasma levels of metabolites in the metabolic clusters that were significantly altered in ME/CFS (bold in Supplementary Table S4) and MFI scales using Spearman’s correlation tests in all ME/CFS, all controls, female ME/CFS, female controls, male ME/CFS, and male controls. (A) Correlation heatmap, part 1. (B) Correlation heatmap, part 2. (C) Correlation heatmap, part 3.

References

    1. Committee on the Diagnostic Criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Board on the Health of Select Populations. Institute of Medicine . The National Academies Collection: Reports Funded by National Institutes of Health. National Academies Press; Washington, DC, USA: 2015. Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness.
    1. Carruthers B.M., van de Sande M.I., De Meirleir K.L., Klimas N.G., Broderick G., Mitchell T., Staines D., Powles A.C., Speight N., Vallings R., et al. Myalgic encephalomyelitis: International Consensus Criteria. J. Intern. Med. 2011;270:327–338. doi: 10.1111/j.1365-2796.2011.02428.x.
    1. Jason L.A., Mirin A.A. Updating the National Academy of Medicine ME/CFS prevalence and economic impact figures to account for population growth and inflation. Fatigue Biomed. Health Behav. 2021;9:9–13. doi: 10.1080/21641846.2021.1878716.
    1. Haney E., Smith M.E., McDonagh M., Pappas M., Daeges M., Wasson N., Nelson H.D. Diagnostic Methods for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Systematic Review for a National Institutes of Health Pathways to Prevention Workshop. Ann. Intern. Med. 2015;162:834–840. doi: 10.7326/M15-0443.
    1. Scheibenbogen C., Freitag H., Blanco J., Capelli E., Lacerda E., Authier J., Meeus M., Castro Marrero J., 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. doi: 10.1186/s12967-017-1263-z.
    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. Armstrong C.W., McGregor N.R., Sheedy J.R., Buttfield I., Butt H.L., Gooley P.R. NMR metabolic profiling of serum identifies amino acid disturbances in chronic fatigue syndrome. Clin. Chim. Acta. 2012;413:1525–1531. doi: 10.1016/j.cca.2012.06.022.
    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. Germain A., Barupal D.K., Levine S.M., Hanson M.R. Comprehensive Circulatory Metabolomics in ME/CFS Reveals Disrupted Metabolism of Acyl Lipids and Steroids. Metabolites. 2020;10:34. doi: 10.3390/metabo10010034.
    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. 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. Naviaux R.K., Naviaux J.C., Li K., 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., 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. Valdez A.R., Hancock E.E., Adebayo S., Kiernicki D.J., Proskauer D., Attewell J.R., Bateman L., DeMaria A., Jr., 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. 2018;6:412. doi: 10.3389/fped.2018.00412.
    1. Milivojevic M., Che X., Bateman L., Cheng A., Garcia B.A., Hornig M., Huber M., Klimas N.G., Lee B., Lee H., et al. Plasma proteomic profiling suggests an association between antigen driven clonal B cell expansion and ME/CFS. PLoS ONE. 2020;15:e0236148. doi: 10.1371/journal.pone.0236148.
    1. Tomic S., Brkic S., Maric D., Mikic A.N. Lipid and protein oxidation in female patients with chronic fatigue syndrome. Arch. Med. Sci. 2012;8:886–891. doi: 10.5114/aoms.2012.31620.
    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: 10.1046/j.1525-1497.2001.03419.x.
    1. Giloteaux L., Goodrich J.K., Walters W.A., Levine S.M., Ley R.E., Hanson M.R. Reduced diversity and altered composition of the gut microbiome in individuals with myalgic encephalomyelitis/chronic fatigue syndrome. Microbiome. 2016;4:30. doi: 10.1186/s40168-016-0171-4.
    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. Neuro. Endocrinol. Lett. 2015;36:439–446.
    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. Makowski D., Ben-Shachar M.S., Chen S.H.A., Ludecke D. Indices of Effect Existence and Significance in the Bayesian Framework. Front. Psychol. 2019;10:2767. doi: 10.3389/fpsyg.2019.02767.
    1. Jeffreys H. Theory of Probability. 3rd ed. Clarendon Press; Oxford, UK: 1961.
    1. Tibshirani R. Regression Shrinkage and Selection Via the Lasso. J. R. Stat. Soc. Series B Stat. Methodol. 1996;58:267–288. doi: 10.1111/j.2517-6161.1996.tb02080.x.
    1. Zou H. The Adaptive Lasso and Its Oracle Properties. J. Am. Stat. Assoc. 2012;101:1418–1429. doi: 10.1198/016214506000000735.
    1. Breiman L. Random Forests. Mach. Learn. 2001;45:5–32. doi: 10.1023/A:1010933404324.
    1. Chen T., Guestrin C. XGBoost: A Scalable Tree Boosting System; Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; San Francisco, CA, USA. 13–17August 2016; pp. 785–794.
    1. Hoel F., Hoel A., Pettersen I.K., Rekeland I.G., Risa K., Alme K., Sorland K., Fossa A., Lien K., Herder I., et al. A map of metabolic phenotypes in patients with myalgic encephalomyelitis/chronic fatigue syndrome. JCI Insight. 2021;6:e149217. doi: 10.1172/jci.insight.149217.
    1. Messias M.C.F., Mecatti G.C., Priolli D.G., de Oliveira Carvalho P. Plasmalogen lipids: Functional mechanism and their involvement in gastrointestinal cancer. Lipids Health Dis. 2018;17:41. doi: 10.1186/s12944-018-0685-9.
    1. Wanders R.J., Poll-The B.T. Role of peroxisomes in human lipid metabolism and its importance for neurological development. Neurosci. Lett. 2017;637:11–17. doi: 10.1016/j.neulet.2015.06.018.
    1. Honsho M., Fujiki Y. Plasmalogen homeostasis-regulation of plasmalogen biosynthesis and its physiological consequence in mammals. FEBS Lett. 2017;591:2720–2729. doi: 10.1002/1873-3468.12743.
    1. Wanders R.J., Waterham H.R., Ferdinandusse S. Metabolic Interplay between Peroxisomes and Other Subcellular Organelles Including Mitochondria and the Endoplasmic Reticulum. Front. Cell Dev. Biol. 2016;3:83. doi: 10.3389/fcell.2015.00083.
    1. Missailidis D., Sanislav O., Allan C.Y., Smith P.K., Annesley S.J., Fisher P.R. Dysregulated Provision of Oxidisable Substrates to the Mitochondria in ME/CFS Lymphoblasts. Int. J. Mol. Sci. 2021;22:2046. doi: 10.3390/ijms22042046.
    1. Kujawski S., Cossington J., Slomko J., Zawadka-Kunikowska M., Tafil-Klawe M., Klawe J.J., Buszko K., Jakovljevic D.G., Kozakiewicz M., Morten K.J., et al. Relationship between Cardiopulmonary, Mitochondrial and Autonomic Nervous System Function Improvement after an Individualised Activity Programme upon Chronic Fatigue Syndrome Patients. J. Clin. Med. 2021;10:1542. doi: 10.3390/jcm10071542.
    1. Flanagan J.L., Simmons P.A., Vehige J., Willcox M.D., Garrett Q. Role of carnitine in disease. Nutr. Metab. 2010;7:30. doi: 10.1186/1743-7075-7-30.
    1. Li J.L., Wang Q.Y., Luan H.Y., Kang Z.C., Wang C.B. Effects of L-carnitine against oxidative stress in human hepatocytes: Involvement of peroxisome proliferator-activated receptor alpha. J. Biomed. Sci. 2012;19:32. doi: 10.1186/1423-0127-19-32.
    1. Vacha G.M., Giorcelli G., Siliprandi N., Corsi M. Favorable effects of L-carnitine treatment on hypertriglyceridemia in hemodialysis patients: Decisive role of low levels of high-density lipoprotein-cholesterol. Am. J. Clin. Nutr. 1983;38:532–540. doi: 10.1093/ajcn/38.4.532.
    1. Violante S., Ijlst L., Te Brinke H., Koster J., Tavares de Almeida I., Wanders R.J., Ventura F.V., Houten S.M. Peroxisomes contribute to the acylcarnitine production when the carnitine shuttle is deficient. Biochim. Biophys. Acta. 2013;1831:1467–1474. doi: 10.1016/j.bbalip.2013.06.007.
    1. Demarquoy J., Le Borgne F. Crosstalk between mitochondria and peroxisomes. World J. Biol. Chem. 2015;6:301–309. doi: 10.4331/wjbc.v6.i4.301.
    1. Paul B.D., Lemle M.D., Komaroff A.L., Snyder S.H. Redox imbalance links COVID-19 and myalgic encephalomyelitis/chronic fatigue syndrome. Proc. Natl. Acad. Sci. USA. 2021;118:e2024358118. doi: 10.1073/pnas.2024358118.
    1. Sperka-Gottlieb C.D., Hermetter A., Paltauf F., Daum G. Lipid topology and physical properties of the outer mitochondrial membrane of the yeast, Saccharomyces cerevisiae. Biochim. Biophys. Acta. 1988;946:227–234. doi: 10.1016/0005-2736(88)90397-5.
    1. Zinser E., Sperka-Gottlieb C.D., Fasch E.V., Kohlwein S.D., Paltauf F., Daum G. Phospholipid synthesis and lipid composition of subcellular membranes in the unicellular eukaryote Saccharomyces cerevisiae. J. Bacteriol. 1991;173:2026–2034. doi: 10.1128/jb.173.6.2026-2034.1991.
    1. Gibellini F., Smith T.K. The Kennedy pathway--De novo synthesis of phosphatidylethanolamine and phosphatidylcholine. IUBMB Life. 2010;62:414–428. doi: 10.1002/iub.354.
    1. Birner R., Burgermeister M., Schneiter R., Daum G. Roles of phosphatidylethanolamine and of its several biosynthetic pathways in Saccharomyces cerevisiae. Mol. Biol. Cell. 2001;12:997–1007. doi: 10.1091/mbc.12.4.997.
    1. Dowhan W., Bogdanov M. Lipid-dependent membrane protein topogenesis. Annu. Rev. Biochem. 2009;78:515–540. doi: 10.1146/annurev.biochem.77.060806.091251.
    1. Furt F., Moreau P. Importance of lipid metabolism for intracellular and mitochondrial membrane fusion/fission processes. Int. J. Biochem. Cell Biol. 2009;41:1828–1836. doi: 10.1016/j.biocel.2009.02.005.
    1. Schuler M.H., Di Bartolomeo F., Martensson C.U., Daum G., Becker T. Phosphatidylcholine Affects Inner Membrane Protein Translocases of Mitochondria. J. Biol. Chem. 2016;291:18718–18729. doi: 10.1074/jbc.M116.722694.
    1. Schuler M.H., Di Bartolomeo F., Bottinger L., Horvath S.E., Wenz L.S., Daum G., Becker T. Phosphatidylcholine affects the role of the sorting and assembly machinery in the biogenesis of mitochondrial beta-barrel proteins. J. Biol. Chem. 2015;290:26523–26532. doi: 10.1074/jbc.M115.687921.
    1. Yoda E., Hachisu K., Taketomi Y., Yoshida K., Nakamura M., Ikeda K., Taguchi R., Nakatani Y., Kuwata H., Murakami M., et al. Mitochondrial dysfunction and reduced prostaglandin synthesis in skeletal muscle of Group VIB Ca2+-independent phospholipase A2gamma-deficient mice. J. Lipid. Res. 2010;51:3003–3015. doi: 10.1194/jlr.M008060.
    1. Dawaliby R., Trubbia C., Delporte C., Masureel M., Van Antwerpen P., Kobilka B.K., Govaerts C. Allosteric regulation of G protein-coupled receptor activity by phospholipids. Nat. Chem. Biol. 2016;12:35–39. doi: 10.1038/nchembio.1960.
    1. Li Z., Vance D.E. Phosphatidylcholine and choline homeostasis. J. Lipid Res. 2008;49:1187–1194. doi: 10.1194/jlr.R700019-JLR200.
    1. Momchilova A., Markovska T. Phosphatidylethanolamine and phosphatidylcholine are sources of diacylglycerol in ras-transformed NIH 3T3 fibroblasts. Int. J. Biochem. Cell Biol. 1999;31:311–318. doi: 10.1016/S1357-2725(98)00111-3.
    1. Okamoto Y., Morishita J., Tsuboi K., Tonai T., Ueda N. Molecular characterization of a phospholipase D generating anandamide and its congeners. J. Biol. Chem. 2004;279:5298–5305. doi: 10.1074/jbc.M306642200.
    1. Mathias S., Kolesnick R. Ceramide: A novel second messenger. Adv. Lipid Res. 1993;25:65–90.
    1. Cabral-Marques O., Marques A., Giil L.M., De Vito R., Rademacher J., Gunther J., Lange T., Humrich J.Y., Klapa S., Schinke S., et al. GPCR-specific autoantibody signatures are associated with physiological and pathological immune homeostasis. Nat. Commun. 2018;9:5224. doi: 10.1038/s41467-018-07598-9.
    1. Loebel M., Grabowski P., Heidecke H., Bauer S., Hanitsch L.G., Wittke K., Meisel C., Reinke P., Volk H.D., Fluge O., et al. Antibodies to beta adrenergic and muscarinic cholinergic receptors in patients with Chronic Fatigue Syndrome. Brain Behav. Immun. 2016;52:32–39. doi: 10.1016/j.bbi.2015.09.013.
    1. Wirth K., Scheibenbogen C. A Unifying Hypothesis of the Pathophysiology of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): Recognitions from the finding of autoantibodies against ss2-adrenergic receptors. Autoimmun. Rev. 2020;19:102527. doi: 10.1016/j.autrev.2020.102527.
    1. Martinez-Reyes I., Chandel N.S. Mitochondrial TCA cycle metabolites control physiology and disease. Nat. Commun. 2020;11:102. doi: 10.1038/s41467-019-13668-3.
    1. Zhang Y., Zhang M., Zhu W., Yu J., Wang Q., Zhang J., Cui Y., Pan X., Gao X., Sun H. Succinate accumulation induces mitochondrial reactive oxygen species generation and promotes status epilepticus in the kainic acid rat model. Redox Biol. 2020;28:101365. doi: 10.1016/j.redox.2019.101365.
    1. Palmieri E.M., Gonzalez-Cotto M., Baseler W.A., Davies L.C., Ghesquiere B., Maio N., Rice C.M., Rouault T.A., Cassel T., Higashi R.M., et al. Nitric oxide orchestrates metabolic rewiring in M1 macrophages by targeting aconitase 2 and pyruvate dehydrogenase. Nat. Commun. 2020;11:698. doi: 10.1038/s41467-020-14433-7.
    1. Morris G., Berk M., Klein H., Walder K., Galecki P., Maes M. Nitrosative Stress, Hypernitrosylation, and Autoimmune Responses to Nitrosylated Proteins: New Pathways in Neuroprogressive Disorders Including Depression and Chronic Fatigue Syndrome. Mol. Neurobiol. 2017;54:4271–4291. doi: 10.1007/s12035-016-9975-2.
    1. Morris G., Maes M. Mitochondrial dysfunctions in myalgic encephalomyelitis/chronic fatigue syndrome explained by activated immuno-inflammatory, oxidative and nitrosative stress pathways. Metab. Brain Dis. 2014;29:19–36. doi: 10.1007/s11011-013-9435-x.
    1. Gerlach B.D., Marinello M., Heinz J., Rymut N., Sansbury B.E., Riley C.O., Sadhu S., Hosseini Z., Kojima Y., Tang D.D., et al. Resolvin D1 promotes the targeting and clearance of necroptotic cells. Cell Death Differ. 2020;27:525–539. doi: 10.1038/s41418-019-0370-1.
    1. Komaroff A.L. Inflammation correlates with symptoms in chronic fatigue syndrome. Proc. Natl. Acad. Sci. USA. 2017;114:8914–8916. doi: 10.1073/pnas.1712475114.
    1. Nakatomi Y., Kuratsune H., Watanabe Y. Neuroinflammation in the Brain of Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Brain Nerve. 2018;70:19–25.
    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. Carruthers B.M., Jain A.K., De Meirleir K.L., Peterson D.L., Klimas N.G., Lerner A.M., Bested A.C., Flor-Henry P., Joshi P., Powles A.C.P., et al. Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. J. Chronic Fatigue Syndr. 2003;11:7–115. doi: 10.1300/J092v11n01_02.
    1. Jason L.A., Evans M., Porter N., Brown M., Brown A., Hunnell J., Anderson V., Lerch A., De Meirleir K., Friedberg F. The Development of a Revised Canadian Myalgic Encephalomyelitis Chronic Fatigue Syndrome Case Definition. Am. J. Biochem. Biotechnol. 2010;6:120–135. doi: 10.3844/ajbbsp.2010.120.135.
    1. Buysse D.J., Reynolds C.F., 3rd, Monk T.H., Berman S.R., Kupfer D.J. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193–213. doi: 10.1016/0165-1781(89)90047-4.
    1. Ware J.E., Jr., Sherbourne C.D. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med. Care. 1992;30:473–483. doi: 10.1097/00005650-199206000-00002.
    1. Smets E.M., Garssen B., Bonke B., De Haes J.C. The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. J. Psychosom. Res. 1995;39:315–325. doi: 10.1016/0022-3999(94)00125-O.
    1. Fiehn O. Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling. Curr. Protoc. Mol. Biol. 2016;114:30.4.1–30.4.32. doi: 10.1002/0471142727.mb3004s114.
    1. Kind T., Wohlgemuth G., Lee D.Y., Lu Y., Palazoglu M., Shahbaz S., Fiehn O. FiehnLib: Mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal. Chem. 2009;81:10038–10048. doi: 10.1021/ac9019522.
    1. Cajka T., Smilowitz J.T., Fiehn O. Validating Quantitative Untargeted Lipidomics Across Nine Liquid Chromatography-High-Resolution Mass Spectrometry Platforms. Anal. Chem. 2017;89:12360–12368. doi: 10.1021/acs.analchem.7b03404.
    1. Tsugawa H., Cajka T., Kind T., Ma Y., Higgins B., Ikeda K., Kanazawa M., VanderGheynst J., Fiehn O., Arita M. MS-DIAL: Data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods. 2015;12:523–526. doi: 10.1038/nmeth.3393.
    1. Kind T., Liu K.H., Lee D.Y., DeFelice B., Meissen J.K., Fiehn O. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat. Methods. 2013;10:755–758. doi: 10.1038/nmeth.2551.
    1. Bakovic M., Fullerton M.D., Michel V. Metabolic and molecular aspects of ethanolamine phospholipid biosynthesis: The role of CTP:phosphoethanolamine cytidylyltransferase (Pcyt2) Biochem. Cell Biol. 2007;85:283–300. doi: 10.1139/O07-006.
    1. DeFelice B.C., Mehta S.S., Samra S., Cajka T., Wancewicz B., Fahrmann J.F., Fiehn O. Mass Spectral Feature List Optimizer (MS-FLO): A Tool To Minimize False Positive Peak Reports in Untargeted Liquid Chromatography-Mass Spectroscopy (LC-MS) Data Processing. Anal. Chem. 2017;89:3250–3255. doi: 10.1021/acs.analchem.6b04372.
    1. Fan S., Kind T., Cajka T., Hazen S.L., Tang W.H.W., Kaddurah-Daouk R., Irvin M.R., Arnett D.K., Barupal D.K., Fiehn O. Systematic Error Removal Using Random Forest for Normalizing Large-Scale Untargeted Lipidomics Data. Anal. Chem. 2019;91:3590–3596. doi: 10.1021/acs.analchem.8b05592.
    1. Benjamini Y., Hochberg Y. Controlling the False Discovery Rate-A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Series B Stat. Methodol. 1995;57:289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x.
    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.
    1. Goodrich B., Gabry J., Ali I., Brilleman S. Rstanarm: Bayesian Applied Regression Modeling via Stan. [(accessed on 13 April 2021)]. Available online:
    1. Makowski D., Ben-Shachar M.S., Lüdecke D. bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayseian Framework. J. Open Source Softw. 2019;4:1541. doi: 10.21105/joss.01541.
    1. Gelman A., Jakulin A., Pittau M.G., Su Y.-S. A weakly informative default prior distribution for logistic and other regression models. Ann. Appl. Stat. 2008;2:1360–1383. doi: 10.1214/08-AOAS191.
    1. Muth C., Oravecz Z., Gabry J. User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. Quant. Meth. Psych. 2018;14:99–119. doi: 10.20982/tqmp.14.2.p099.
    1. Fan J., Li R. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties. J. Am. Stat. Assoc. 2001;96:1348–1360. doi: 10.1198/016214501753382273.
    1. Hoeting J.A., Madigan D., Raftery A.E., Volinsky C.T. Bayesian Model Averaging: A Tutorial. Stat. Sci. 1999;14:382–401.

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe